From 972585b9011658c3e6c95775d8b7f650fb5fe96f Mon Sep 17 00:00:00 2001 From: Konstantin Pandl <16907747+kpandl@users.noreply.github.com> Date: Wed, 30 Jul 2025 14:48:41 +0200 Subject: [PATCH 01/31] initialize workshop playbook --- zkml-research/KYA_face/mnist_dataset.ipynb | 3265 ++++++++++++++++++++ 1 file changed, 3265 insertions(+) create mode 100644 zkml-research/KYA_face/mnist_dataset.ipynb diff --git a/zkml-research/KYA_face/mnist_dataset.ipynb b/zkml-research/KYA_face/mnist_dataset.ipynb new file mode 100644 index 0000000..d0c3e09 --- /dev/null +++ b/zkml-research/KYA_face/mnist_dataset.ipynb @@ -0,0 +1,3265 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demo of KYA face identification - MLP neural network training and Leo transpilation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overview of steps\n", + "* Ensure Leo and Python library installation\n", + "* Add your own image (optional)\n", + "* Load the dataset and extract the features\n", + "* Split into a training and test dataset\n", + "* Train the model\n", + "* Evaluate the model\n", + "* PCA feature transformation\n", + "* Re-train and evaluate the model\n", + "* Transpilation to Leo\n", + "* Test execution of the Leo program\n", + "* Deployment of the Leo program" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## TODO Ensure Leo and Python library installation\n", + "For this Jupyter notebook to run successfully, you need to ensure Leo and selected Python libraries are installed. If you haven't done already ..." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/lib/python3.13/site-packages/face_recognition_models/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import resource_filename\n" + ] + } + ], + "source": [ + "import os\n", + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "import face_recognition\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add your own image (optional)\n", + "In this notebook, we use 3 JPG images of Albert Einstein as the positive class.\n", + "You can replace these with 3 JPG images of yourself - place the files `1.jpg`, `2.jpg`, `3.jpg` in the folder.\n", + "\n", + "The following code ensures the files inside the positive class folder are correctly set up." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current working directory: /Users/kp/dev/python-sdk3/zkml-research/KYA_face\n" + ] + } + ], + "source": [ + "cwd = os.getcwd()\n", + "print(f\"Current working directory: {cwd}\")\n", + "\n", + "positive_dir_path = os.path.join(cwd, 'face_images', 'positive')\n", + "if not os.path.isdir(positive_dir_path):\n", + " raise FileNotFoundError(f\"Directory not found: {positive_dir_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found expected files: ['1.jpg', '2.jpg', '3.jpg']\n" + ] + } + ], + "source": [ + "files = sorted(f for f in os.listdir(positive_dir_path)\n", + " if os.path.isfile(os.path.join(positive_dir_path, f)))\n", + "expected = ['1.jpg', '2.jpg', '3.jpg']\n", + "if files != expected:\n", + " raise ValueError(f\"Expected {expected}, but found {files}\")\n", + "print(f\"Found expected files: {files}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image_path = os.path.join(positive_dir_path, '3.jpg')\n", + "img = Image.open(image_path)\n", + "plt.imshow(img)\n", + "plt.axis('off')\n", + "plt.title('3.jpg')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the dataset and extract the features" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def iter_face_embeddings(directory: Path, label: int):\n", + " \"\"\"\n", + " Yields dicts with keys:\n", + " - 'filename': str\n", + " - 'label': int (1=positive, 0=negative)\n", + " - 'embedding': np.ndarray shape (128,)\n", + " \"\"\"\n", + " for img_path in sorted(directory.iterdir()):\n", + " if img_path.suffix.lower() not in {'.jpg', '.jpeg', '.png'}:\n", + " continue\n", + " image = face_recognition.load_image_file(str(img_path))\n", + " encs = face_recognition.face_encodings(image)\n", + " if not encs:\n", + " print(f\"[warn] no face in {img_path.name}, skipping\")\n", + " continue\n", + " yield {\n", + " 'filename': img_path.name,\n", + " 'label': label,\n", + " 'embedding': encs[0]\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[warn] no face in 24_0_2_20170116174324623.jpg, skipping\n", + "[warn] no face in 6_0_0_20170110213600515.jpg, skipping\n" + ] + } + ], + "source": [ + "# also define negative_dir_path\n", + "negative_dir_path = os.path.join(cwd, 'face_images', 'negative')\n", + "\n", + "# collect records\n", + "records = list(iter_face_embeddings(Path(positive_dir_path), label=1)) \\\n", + " + list(iter_face_embeddings(Path(negative_dir_path), label=0))\n", + "\n", + "# DataFrame with metadata + raw embeddings\n", + "df = pd.DataFrame(records)\n", + "\n", + "# stack embeddings into an (n_samples × 128) array\n", + "X = np.stack(df['embedding'].values)\n", + "y = df['label'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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filenamelabelembedding
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" + ], + "text/plain": [ + " filename label \\\n", + "0 1.jpg 1 \n", + "1 2.jpg 1 \n", + "2 3.jpg 1 \n", + "3 12_0_0_20170110215739155.jpg 0 \n", + "4 14_0_1_20170117141604244.jpg 0 \n", + "\n", + " embedding \n", + "0 [-0.13916254043579102, 0.06765440106391907, 0.... \n", + "1 [-0.10024195909500122, 0.060482949018478394, 0... \n", + "2 [-0.1615755558013916, 0.07210954278707504, 0.0... \n", + "3 [-0.0820397362112999, 0.027357343584299088, 0.... \n", + "4 [-0.21727801859378815, 0.1069168969988823, 0.0... " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split into a training and test dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training samples: 67 (pos=2, neg=65)\n", + " Testing samples: 34 (pos=1, neg=33)\n" + ] + }, + { + "data": { + "text/plain": [ + "( filename label\n", + " 0 1.jpg 1\n", + " 1 2.jpg 1\n", + " 2 58_0_3_20170119211659305.jpg 0\n", + " 3 18_1_0_20170109213623055.jpg 0\n", + " 4 32_0_1_20170113133956457.jpg 0\n", + " .. ... ...\n", + " 62 40_0_3_20170117154652454.jpg 0\n", + " 63 46_1_1_20170116223430582.jpg 0\n", + " 64 25_1_0_20170117142815331.jpg 0\n", + " 65 68_0_0_20170105173736735.jpg 0\n", + " 66 35_0_3_20170117154729846.jpg 0\n", + " \n", + " [67 rows x 2 columns],\n", + " filename label\n", + " 0 3.jpg 1\n", + " 1 40_1_2_20170116161916676.jpg 0\n", + " 2 30_0_1_20170113195438285.jpg 0\n", + " 3 87_0_0_20170111222120006.jpg 0\n", + " 4 26_0_1_20170113151702303.jpg 0\n", + " 5 54_0_0_20170104165859441.jpg 0\n", + " 6 54_1_0_20170117191133235.jpg 0\n", + " 7 42_0_0_20170112220250648.jpg 0\n", + " 8 31_1_1_20170112231608655.jpg 0\n", + " 9 23_0_3_20170119164041958.jpg 0\n", + " 10 12_0_0_20170110215739155.jpg 0\n", + " 11 28_1_0_20170117180704448.jpg 0\n", + " 12 50_0_2_20170116191755793.jpg 0\n", + " 13 32_1_1_20170113011625824.jpg 0\n", + " 14 27_1_3_20170104223343215.jpg 0\n", + " 15 32_0_0_20170117140244970.jpg 0\n", + " 16 17_1_4_20170104001810179.jpg 0\n", + " 17 26_1_2_20170116182615565.jpg 0\n", + " 18 24_1_2_20170116174523538.jpg 0\n", + " 19 60_1_0_20170110154325940.jpg 0\n", + " 20 47_0_1_20170117021247110.jpg 0\n", + " 21 34_0_0_20170117105018453.jpg 0\n", + " 22 46_0_0_20170117190143691.jpg 0\n", + " 23 44_0_3_20170119200511259.jpg 0\n", + " 24 25_1_0_20170119172029833.jpg 0\n", + " 25 29_1_1_20170117105207326.jpg 0\n", + " 26 28_1_2_20170105000553851.jpg 0\n", + " 27 23_0_1_20170117194052028.jpg 0\n", + " 28 53_1_3_20170119205926527.jpg 0\n", + " 29 24_0_3_20170119164638750.jpg 0\n", + " 30 42_0_2_20170104192842031.jpg 0\n", + " 31 80_1_0_20170110131934630.jpg 0\n", + " 32 28_1_0_20170116222126409.jpg 0\n", + " 33 28_0_0_20170116212026440.jpg 0)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "from sklearn.neural_network import MLPClassifier\n", + "\n", + "# 1) Positive class: first two → train, last one → test\n", + "df_pos = df[df['label'] == 1].reset_index(drop=True)\n", + "train_pos = df_pos.iloc[[0, 1]] # 1.jpg, 2.jpg\n", + "test_pos = df_pos.iloc[[2]] # 3.jpg\n", + "\n", + "# 2) Negative class: random 2/3 train, 1/3 test (seed=42)\n", + "df_neg = df[df['label'] == 0].reset_index(drop=True)\n", + "train_neg, test_neg = train_test_split(\n", + " df_neg,\n", + " test_size=1/3,\n", + " random_state=42,\n", + " shuffle=True\n", + ")\n", + "\n", + "# 3) Combine back together\n", + "train_df = pd.concat([train_pos, train_neg]).reset_index(drop=True)\n", + "test_df = pd.concat([test_pos, test_neg]).reset_index(drop=True)\n", + "\n", + "# 4) Extract X / y arrays\n", + "X_train = np.stack(train_df['embedding'].values)\n", + "y_train = np.eye(2)[train_df['label'].values]\n", + "\n", + "X_test = np.stack(test_df['embedding'].values)\n", + "y_test = np.eye(2)[test_df['label'].values]\n", + "\n", + "# 5) Quick sanity check\n", + "print(f\"Training samples: {len(train_df)} (pos={len(train_pos)}, neg={len(train_neg)})\")\n", + "print(f\" Testing samples: {len(test_df)} (pos={len(test_pos)}, neg={len(test_neg)})\")\n", + "train_df[['filename','label']], test_df[['filename','label']]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train mean (first 5 features): [ 2.64092231e-17 5.30255773e-17 1.63012224e-16 -4.97114787e-17\n", + " -9.44518096e-17]\n", + "Train std (first 5 features): [1. 1. 1. 1. 1.]\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# 1) Initialize scaler\n", + "scaler = StandardScaler()\n", + "\n", + "# 2) Fit on training data only, then transform both\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# 3) (Optional) check means/vars\n", + "print(\"Train mean (first 5 features):\", X_train_scaled.mean(axis=0)[:5])\n", + "print(\"Train std (first 5 features):\", X_train_scaled.std(axis=0)[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train the model" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/lib/python3.13/site-packages/sklearn/neural_network/_multilayer_perceptron.py:781: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
MLPClassifier(hidden_layer_sizes=(65,), max_iter=100, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" + ], + "text/plain": [ + "MLPClassifier(hidden_layer_sizes=(65,), max_iter=100, random_state=42)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf_large = MLPClassifier(hidden_layer_sizes=(65,), max_iter=100, random_state=42)\n", + "clf_large.fit(X_train_scaled, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the model" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train accuracy: 1.0\n", + "Test accuracy: 1.0\n" + ] + } + ], + "source": [ + "print(\"Train accuracy:\", clf_large.score(X_train_scaled, y_train))\n", + "print(\"Test accuracy:\", clf_large.score(X_test_scaled, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PCA feature transformation" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train_pca shape: (67, 32)\n", + "X_test_pca shape: (34, 32)\n", + "Cumulative explained variance (first 5 components): [0.13134312 0.25755996 0.33214079 0.39133033 0.44493415]\n", + "Total variance retained: 0.9277635063358127\n" + ] + } + ], + "source": [ + "# Cell X: PCA dimensionality reduction\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# 1) Initialize PCA (fit only on training data)\n", + "pca = PCA(n_components=32, random_state=42)\n", + "\n", + "# 2) Fit on X_train and transform both train & test\n", + "X_train_pca = pca.fit_transform(X_train)\n", + "X_test_pca = pca.transform(X_test)\n", + "\n", + "# 3) Check shapes to confirm reduction\n", + "print(\"X_train_pca shape:\", X_train_pca.shape) # (n_train_samples, 32)\n", + "print(\"X_test_pca shape:\", X_test_pca.shape) # (n_test_samples, 32)\n", + "\n", + "# (Optional) Examine how much variance is retained\n", + "explained = pca.explained_variance_ratio_.cumsum()\n", + "print(\"Cumulative explained variance (first 5 components):\", explained[:5])\n", + "print(\"Total variance retained:\", explained[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Post-PCA train means (first 5 comps): [-4.97114787e-18 1.65704929e-17 -6.62819716e-18 -2.89983626e-17\n", + " 3.31409858e-18]\n", + "Post-PCA train stds (first 5 comps): [1. 1. 1. 1. 1.]\n" + ] + } + ], + "source": [ + "# Cell Y: Re-standardize PCA outputs before model training\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# 1) Initialize a new scaler\n", + "scaler_pca = StandardScaler()\n", + "\n", + "# 2) Fit on the PCA‐transformed training data, then apply to both\n", + "X_train_pca_scaled = scaler_pca.fit_transform(X_train_pca)\n", + "X_test_pca_scaled = scaler_pca.transform(X_test_pca)\n", + "\n", + "# 3) Quick sanity check: zero mean/unit var on train\n", + "print(\"Post-PCA train means (first 5 comps):\", \n", + " X_train_pca_scaled.mean(axis=0)[:5])\n", + "print(\"Post-PCA train stds (first 5 comps):\", \n", + " X_train_pca_scaled.std(axis=0)[:5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Re-train and evaluate the model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/homebrew/lib/python3.13/site-packages/sklearn/neural_network/_multilayer_perceptron.py:781: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
MLPClassifier(hidden_layer_sizes=(17,), max_iter=100, random_state=42)
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" + ], + "text/plain": [ + "MLPClassifier(hidden_layer_sizes=(17,), max_iter=100, random_state=42)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf_small = MLPClassifier(hidden_layer_sizes=(17,), max_iter=100, random_state=42)\n", + "clf_small.fit(X_train_pca_scaled, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train accuracy: 0.9850746268656716\n", + "Test accuracy: 0.9117647058823529\n" + ] + } + ], + "source": [ + "print(\"Train accuracy:\", clf_small.score(X_train_pca_scaled, y_train))\n", + "print(\"Test accuracy:\", clf_small.score(X_test_pca_scaled, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transpilation to Leo" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from zkml import LeoTranspiler\n", + "\n", + "# Transpile the NN into Leo code\n", + "lt = LeoTranspiler(\n", + " model=clf_small\n", + ")\n", + "leo_project_path = os.path.join(os.getcwd(), \"/tmp/mnist\")\n", + "leo_project_name = \"sklearn_mlp_mnist_1\"\n", + "lt.to_leo(\n", + " path=leo_project_path, project_name=leo_project_name, fixed_point_scaling_factor=16\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:No validation_data passed to the transpiler, thus, no information available of dataset shape. Passed input sample for run is treated as a single data point\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "python_predictions [[0 1]]\n", + "proba_list [[0.43960105 0.53485759]]\n", + "decimal output [-0.27880859375, 0.108154296875]\n", + "output [-1142, 443]\n", + "input ['{ x0: -21i64, x1: 6i64 }', '{ x0: 13i64, x1: 24i64 }', '{ x0: 0i64, x1: -11i64 }', '{ x0: -7i64, x1: -18i64 }', '{ x0: 3i64, x1: -17i64 }', '{ x0: 12i64, x1: -15i64 }', '{ x0: -15i64, x1: 28i64 }', '{ x0: -1i64, x1: 2i64 }', '{ x0: 8i64, x1: 10i64 }', '{ x0: 29i64, x1: -2i64 }', '{ x0: 2i64, x1: 8i64 }', '{ x0: 9i64, x1: -8i64 }', '{ x0: 9i64, x1: 24i64 }', '{ x0: 2i64, x1: -5i64 }', '{ x0: -8i64, x1: 7i64 }', '{ x0: -15i64, x1: -8i64 }']\n", + "Sigmoid output: (0.4307458893450617, 0.5270122483693945)\n", + "Constraints: 269822\n", + "Runtime for one instance: 2.605588436126709 seconds\n", + "[1.]\n" + ] + } + ], + "source": [ + "#print(y_test[0])\n", + "#print(y_test[1])\n", + "#print(y_test[2])\n", + "\n", + "\n", + "# Compute the accuracy of the Leo program and the Python program on the test set\n", + "num_test_samples = len(X_test_pca_scaled)\n", + "\n", + "# let's limit the number of test stamples to 10 to make the computation faster\n", + "num_test_samples = min(num_test_samples, 1)\n", + "#test_features = X_test_pca[:num_test_samples]\n", + "test_features = X_test_pca_scaled[0:1]\n", + "\n", + "python_predictions = clf_small.predict(test_features)\n", + "\n", + "print(\"python_predictions\", python_predictions)\n", + "\n", + "proba_list = clf_small.predict_proba(test_features)\n", + "\n", + "print(\"proba_list\", proba_list)\n", + "\n", + "leo_predictions = np.zeros(num_test_samples)\n", + "for i in range(num_test_samples):\n", + " lc = lt.run(input=test_features[i])\n", + " print(\"decimal output\", lc.output_decimal)\n", + " print(\"output\", lc.output)\n", + " print(\"input\", lc.input)\n", + " # compute softmax probabilities\n", + " logits = lc.output_decimal # Leo → Python floats\n", + " sigmoid_probs_0 = 1/(1+np.exp(-logits[0])) # element-wise\n", + " sigmoid_probs_1 = 1/(1+np.exp(-logits[1])) # element-wise\n", + " print(\"Sigmoid output:\", (sigmoid_probs_0, sigmoid_probs_1))\n", + " leo_predictions[i] = np.argmax(lc.output_decimal)\n", + "\n", + "print(f\"Constraints: {lc.circuit_constraints}\")\n", + "print(f\"Runtime for one instance: {lc.runtime} seconds\")\n", + "\n", + "print(leo_predictions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ...\n", + "a = 5/0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# URLs and filenames\n", + "file_info = [\n", + " (\n", + " \"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\",\n", + " \"train-images-idx3-ubyte.gz\",\n", + " ),\n", + " (\n", + " \"http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\",\n", + " \"train-labels-idx1-ubyte.gz\",\n", + " ),\n", + " (\n", + " \"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\",\n", + " \"t10k-images-idx3-ubyte.gz\",\n", + " ),\n", + " (\n", + " \"http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\",\n", + " \"t10k-labels-idx1-ubyte.gz\",\n", + " ),\n", + "]\n", + "\n", + "folder_name = \"tmp/mnist\"\n", + "folder_path = os.path.join(os.getcwd(), folder_name)\n", + "\n", + "os.makedirs(folder_path, exist_ok=True) # Create folder if it doesn't exist\n", + "\n", + "# Download and extract each file\n", + "for url, file_name in file_info:\n", + " path_to_save = os.path.join(folder_path, file_name)\n", + " download_and_extract_dataset(url, path_to_save, folder_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define function to read the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def read_idx3_ubyte_image_file(filename):\n", + " \"\"\"Read IDX3-ubyte formatted image data.\"\"\"\n", + " with open(filename, \"rb\") as f:\n", + " magic_num = int.from_bytes(f.read(4), byteorder=\"big\")\n", + " num_images = int.from_bytes(f.read(4), byteorder=\"big\")\n", + " num_rows = int.from_bytes(f.read(4), byteorder=\"big\")\n", + " num_cols = int.from_bytes(f.read(4), byteorder=\"big\")\n", + "\n", + " if magic_num != 2051:\n", + " raise ValueError(f\"Invalid magic number: {magic_num}\")\n", + "\n", + " images = np.zeros((num_images, num_rows, num_cols), dtype=np.uint8)\n", + "\n", + " for i in range(num_images):\n", + " for r in range(num_rows):\n", + " for c in range(num_cols):\n", + " pixel = int.from_bytes(f.read(1), byteorder=\"big\")\n", + " images[i, r, c] = pixel\n", + "\n", + " return images\n", + "\n", + "\n", + "def read_idx1_ubyte_label_file(filename):\n", + " \"\"\"Read IDX1-ubyte formatted label data.\"\"\"\n", + " with open(filename, \"rb\") as f:\n", + " magic_num = int.from_bytes(f.read(4), byteorder=\"big\")\n", + " num_labels = int.from_bytes(f.read(4), byteorder=\"big\")\n", + "\n", + " if magic_num != 2049:\n", + " raise ValueError(f\"Invalid magic number: {magic_num}\")\n", + "\n", + " labels = np.zeros(num_labels, dtype=np.uint8)\n", + "\n", + " for i in range(num_labels):\n", + " labels[i] = int.from_bytes(f.read(1), byteorder=\"big\")\n", + "\n", + " return labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "folder_path = os.path.join(\n", + " os.getcwd(), folder_name\n", + ") # Adjust this path to where you stored the files\n", + "\n", + "train_images = read_idx3_ubyte_image_file(\n", + " os.path.join(folder_path, \"train-images-idx3-ubyte\")\n", + ")\n", + "train_labels = read_idx1_ubyte_label_file(\n", + " os.path.join(folder_path, \"train-labels-idx1-ubyte\")\n", + ")\n", + "test_images = read_idx3_ubyte_image_file(\n", + " os.path.join(folder_path, \"t10k-images-idx3-ubyte\")\n", + ")\n", + "test_labels = read_idx1_ubyte_label_file(\n", + " os.path.join(folder_path, \"t10k-labels-idx1-ubyte\")\n", + ")\n", + "\n", + "print(\n", + " f\"Shape of train_images: {train_images.shape}\"\n", + ") # Should output \"Shape of train_images: (60000, 28, 28)\"\n", + "print(\n", + " f\"Shape of train_labels: {train_labels.shape}\"\n", + ") # Should output \"Shape of train_labels: (60000,)\"\n", + "print(\n", + " f\"Shape of test_images: {test_images.shape}\"\n", + ") # Should output \"Shape of test_images: (10000, 28, 28)\"\n", + "print(\n", + " f\"Shape of test_labels: {test_labels.shape}\"\n", + ") # Should output \"Shape of test_labels: (10000,)\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert the datasets to PyTorch tensors, and get a validation set\n", + "(We use PyTorch instead of sci-kit learn to train sparse neural networks with L1 regularization)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# convertto pytorch tensors\n", + "import torch\n", + "\n", + "train_images_tensor_initial = torch.from_numpy(train_images).float()\n", + "train_labels_tensor_initial = torch.from_numpy(train_labels).long()\n", + "test_images_tensor = torch.from_numpy(test_images).float()\n", + "test_labels_tensor = torch.from_numpy(test_labels).long()\n", + "\n", + "# seed the random number generator\n", + "torch.manual_seed(0)\n", + "\n", + "# shuffle the training dataset\n", + "indices = torch.randperm(train_images_tensor_initial.shape[0])\n", + "train_images_tensor_shuffled = train_images_tensor_initial[indices]\n", + "train_labels_tensor_shuffled = train_labels_tensor_initial[indices]\n", + "\n", + "# get a 10% validation set\n", + "validation_size = int(train_images_tensor_shuffled.shape[0] * 0.1)\n", + "validation_images_tensor = train_images_tensor_shuffled[:validation_size]\n", + "validation_labels_tensor = train_labels_tensor_shuffled[:validation_size]\n", + "train_images_tensor = train_images_tensor_shuffled[validation_size:]\n", + "train_labels_tensor = train_labels_tensor_shuffled[validation_size:]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Train labels tensor shape:\", train_labels_tensor.shape)\n", + "print(\"Validation labels tensor shape:\", validation_labels_tensor.shape)\n", + "print(\"Test labels tensor shape:\", test_labels_tensor.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract feature representations of the dataset\n", + "\n", + "(We transform the bounding box images to 12x12 images, defined by the new_size variable. There is a trade-off in circuit constraints and ML model accuracy. You can increase the image size which will lead to a higher accuracy at the cost of more circuit constraints and thus longer proving times.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def get_bounding_box(img):\n", + " \"\"\"\n", + " Extract the bounding box from an MNIST image.\n", + "\n", + " Args:\n", + " img (np.ndarray): 2D numpy array representing the MNIST image.\n", + "\n", + " Returns:\n", + " (np.ndarray): Cropped image with the bounding box.\n", + " \"\"\"\n", + "\n", + " # convert torch image to numpy array\n", + " img = img.numpy()\n", + "\n", + " # Find the rows and columns where the image has non-zero pixels\n", + " rows = np.any(img, axis=1)\n", + " cols = np.any(img, axis=0)\n", + "\n", + " # Find the first and last row and column indices where the image has non-zero pixels\n", + " rmin, rmax = np.where(rows)[0][[0, -1]]\n", + " cmin, cmax = np.where(cols)[0][[0, -1]]\n", + "\n", + " # Return the cropped image\n", + " return img[rmin : rmax + 1, cmin : cmax + 1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "\n", + "\n", + "def get_resized_datasets(\n", + " train_images_tensor, validation_images_tensor, test_images_tensor, new_size\n", + "):\n", + " num_train = len(train_images_tensor)\n", + " num_test = len(test_images_tensor)\n", + " num_val = len(validation_images_tensor)\n", + "\n", + " train_images_tensor_resized = np.zeros((num_train, new_size**2))\n", + " validation_images_tensor_resized = np.zeros((num_val, new_size**2))\n", + " test_images_tensor_resized = np.zeros((num_test, new_size**2))\n", + "\n", + " for i in range(num_train):\n", + " cropped_image = get_bounding_box(train_images_tensor[i].reshape(28, 28))\n", + " cropped_image_uint8 = np.clip(cropped_image, 0, 255).astype(np.uint8)\n", + " resized_image = cv2.resize(\n", + " cropped_image_uint8, (new_size, new_size), interpolation=cv2.INTER_AREA\n", + " )\n", + " train_images_tensor_resized[i, :] = resized_image.flatten()\n", + "\n", + " for i in range(num_val):\n", + " cropped_image = get_bounding_box(validation_images_tensor[i].reshape(28, 28))\n", + " cropped_image_uint8 = np.clip(cropped_image, 0, 255).astype(np.uint8)\n", + " resized_image = cv2.resize(\n", + " cropped_image_uint8, (new_size, new_size), interpolation=cv2.INTER_AREA\n", + " )\n", + " validation_images_tensor_resized[i, :] = resized_image.flatten()\n", + "\n", + " for i in range(num_test):\n", + " cropped_image = get_bounding_box(test_images_tensor[i].reshape(28, 28))\n", + " cropped_image_uint8 = np.clip(cropped_image, 0, 255).astype(np.uint8)\n", + " resized_image = cv2.resize(\n", + " cropped_image_uint8, (new_size, new_size), interpolation=cv2.INTER_AREA\n", + " )\n", + " test_images_tensor_resized[i, :] = resized_image.flatten()\n", + "\n", + " return (\n", + " train_images_tensor_resized,\n", + " validation_images_tensor_resized,\n", + " test_images_tensor_resized,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_size = 12\n", + "train_images_resized, val_images_resized, test_images_resized = get_resized_datasets(\n", + " train_images_tensor, validation_images_tensor, test_images_tensor, new_size\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's compute the features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_haar_features(image):\n", + " # raise value error if the image is not square\n", + " if image.shape[0] != image.shape[1]:\n", + " raise ValueError(\"The input image must be square.\")\n", + "\n", + " features = []\n", + "\n", + " # Sliding window\n", + " for i in range(0, image.shape[0], 3): # Slide vertically with a step of 3\n", + " for j in range(0, image.shape[0], 3): # Slide horizontally with a step of 3\n", + "\n", + " if i + 6 > image.shape[0] or j + 6 > image.shape[0]:\n", + " continue\n", + "\n", + " # Extract 6x6 window\n", + " window = image[i : i + 6, j : j + 6]\n", + "\n", + " # Horizontal feature\n", + " horizontal_feature_value = np.sum(window[0:3, :]) - np.sum(window[3:6, :])\n", + "\n", + " # Vertical feature\n", + " vertical_feature_value = np.sum(window[:, 0:3]) - np.sum(window[:, 3:6])\n", + "\n", + " features.append(horizontal_feature_value)\n", + " features.append(vertical_feature_value)\n", + "\n", + " return np.array(features)\n", + "\n", + "\n", + "def aspect_ratio(image, threshold=0.5):\n", + " # Threshold the image to create a binary representation\n", + " bin_image = image > threshold\n", + " # Find the bounding box\n", + " row_indices, col_indices = np.nonzero(bin_image)\n", + " max_row, min_row = np.max(row_indices), np.min(row_indices)\n", + " max_col, min_col = np.max(col_indices), np.min(col_indices)\n", + "\n", + " # Calculate the aspect ratio of the bounding box\n", + " width = max_col - min_col + 1\n", + " height = max_row - min_row + 1\n", + "\n", + " if height == 0: # To avoid division by zero\n", + " return 1.0\n", + "\n", + " return width / height\n", + "\n", + "\n", + "from scipy.ndimage import label\n", + "\n", + "\n", + "def num_regions_below_threshold(image, threshold=0.5):\n", + " # Threshold the image so that pixels below the threshold are set to 1\n", + " # and those above the threshold are set to 0.\n", + " bin_image = image < threshold\n", + "\n", + " # Use connected components labeling\n", + " labeled_array, num_features = label(bin_image)\n", + "\n", + " # Return the number of unique regions\n", + " # (subtracting 1 as one of the labels will be the background)\n", + " return num_features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compute datasets\n", + "\n", + "num_train = len(train_images_tensor)\n", + "num_val = len(validation_images_tensor)\n", + "num_test = len(test_images_tensor)\n", + "\n", + "aspect_ratio_train = np.zeros(num_train)\n", + "aspect_ratio_val = np.zeros(num_val)\n", + "aspect_ratio_test = np.zeros(num_test)\n", + "\n", + "num_white_regions_train = np.zeros(num_train)\n", + "num_white_regions_val = np.zeros(num_val)\n", + "num_white_regions_test = np.zeros(num_test)\n", + "\n", + "haar_1 = compute_haar_features(train_images_resized[0].reshape(new_size, new_size))\n", + "len_haar_features = len(haar_1)\n", + "\n", + "haar_train = np.zeros((num_train, len_haar_features))\n", + "haar_val = np.zeros((num_val, len_haar_features))\n", + "haar_test = np.zeros((num_test, len_haar_features))\n", + "\n", + "for i in range(num_train):\n", + " aspect_ratio_train[i] = aspect_ratio(train_images_tensor[i].reshape(28, 28).numpy())\n", + " num_white_regions_train[i] = num_regions_below_threshold(\n", + " train_images_tensor[i].reshape(28, 28)\n", + " )\n", + " haar_train[i, :] = compute_haar_features(\n", + " train_images_resized[i, :].reshape(new_size, new_size)\n", + " )\n", + "\n", + "for i in range(num_val):\n", + " aspect_ratio_val[i] = aspect_ratio(\n", + " validation_images_tensor[i].reshape(28, 28).numpy()\n", + " )\n", + " num_white_regions_val[i] = num_regions_below_threshold(\n", + " validation_images_tensor[i].reshape(28, 28)\n", + " )\n", + " haar_val[i, :] = compute_haar_features(\n", + " val_images_resized[i, :].reshape(new_size, new_size)\n", + " )\n", + "\n", + "for i in range(num_test):\n", + " aspect_ratio_test[i] = aspect_ratio(test_images_tensor[i].reshape(28, 28).numpy())\n", + " num_white_regions_test[i] = num_regions_below_threshold(\n", + " test_images_tensor[i].reshape(28, 28)\n", + " )\n", + " haar_test[i, :] = compute_haar_features(\n", + " test_images_resized[i, :].reshape(new_size, new_size)\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Take a look at the images, and the computed features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "image_id = 0\n", + "\n", + "image = train_images_tensor[image_id].reshape(28, 28)\n", + "\n", + "print(\"Original image\")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.imshow(image, cmap=\"gray\")\n", + "plt.show()\n", + "\n", + "print(\"Image shape\", image.shape)\n", + "print(\"Label\", train_labels_tensor[image_id])\n", + "\n", + "print(\"Resized image\")\n", + "\n", + "image_resized = train_images_resized[image_id].reshape(new_size, new_size)\n", + "\n", + "plt.imshow(image_resized, cmap=\"gray\")\n", + "plt.show()\n", + "\n", + "print(\"Image shape\", image_resized.shape)\n", + "\n", + "print(\"Haar features:\", haar_train[image_id, :])\n", + "print(\"Shape of Haar features:\", haar_train[image_id, :].shape)\n", + "\n", + "print(\"Aspect ratio:\", aspect_ratio_train[image_id])\n", + "print(\"Number of white regions:\", num_white_regions_train[image_id])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's merge all features into one dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# compute datasets\n", + "\n", + "train_features = np.zeros((num_train, len_haar_features + 2))\n", + "val_features = np.zeros((num_val, len_haar_features + 2))\n", + "test_features = np.zeros((num_test, len_haar_features + 2))\n", + "\n", + "for i in range(num_train):\n", + " train_features[i, :] = np.hstack(\n", + " (haar_train[i, :], aspect_ratio_train[i], num_white_regions_train[i])\n", + " )\n", + "\n", + "for i in range(num_val):\n", + " val_features[i, :] = np.hstack(\n", + " (haar_val[i, :], aspect_ratio_val[i], num_white_regions_val[i])\n", + " )\n", + "\n", + "for i in range(num_test):\n", + " test_features[i, :] = np.hstack(\n", + " (haar_test[i, :], aspect_ratio_test[i], num_white_regions_test[i])\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Shape of training features:\", train_features.shape)\n", + "print(\"First training feature vector:\", train_features[0, :])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Normalize the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler()\n", + "train_features_normalized = torch.tensor(scaler.fit_transform(train_features)).float()\n", + "val_features_normalized = torch.tensor(scaler.transform(val_features)).float()\n", + "test_features_normalized = torch.tensor(scaler.transform(test_features)).float()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"First training feature vector (normalized):\", train_features_normalized[0, :])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define the neural network and the training and test function" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "\n", + "def evaluate_model(model):\n", + " model.eval() # Set the model to evaluation mode\n", + " with torch.no_grad():\n", + " test_outputs = model(test_features_normalized)\n", + " _, predicted = torch.max(test_outputs.data, 1)\n", + " accuracy = accuracy_score(test_labels, predicted.numpy())\n", + " print(\"Accuracy:\", accuracy)\n", + " return accuracy\n", + "\n", + "\n", + "# Define the PyTorch neural network\n", + "class SimpleNN(nn.Module):\n", + " def __init__(self, input_dim, hidden_dim, output_dim):\n", + " super(SimpleNN, self).__init__()\n", + " self.fc1 = nn.Linear(input_dim, hidden_dim)\n", + " self.fc2 = nn.Linear(hidden_dim, output_dim)\n", + "\n", + " def forward(self, x):\n", + " x = torch.relu(self.fc1(x))\n", + " x = self.fc2(x)\n", + " return x\n", + "\n", + "\n", + "def train(model):\n", + " model.train()\n", + " criterion = nn.CrossEntropyLoss()\n", + " optimizer = optim.Adam(model.parameters(), lr=0.001)\n", + "\n", + " # Training loop with L1 regularization\n", + " lambda_l1 = 0.0001 # L1 regularization coefficient\n", + "\n", + " validation_losses = []\n", + " epoch = 0\n", + "\n", + " model_states = []\n", + "\n", + " while True:\n", + " optimizer.zero_grad()\n", + " outputs = model(train_features_normalized)\n", + "\n", + " loss = criterion(outputs, train_labels_tensor)\n", + "\n", + " # Add L1 regularization\n", + " l1_reg = torch.tensor(0.0, requires_grad=True)\n", + " for param in model.parameters():\n", + " l1_reg = l1_reg + torch.norm(param, 1)\n", + " loss += lambda_l1 * l1_reg\n", + "\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Print progress\n", + " if (epoch + 1) % 100 == 0:\n", + " print(\n", + " f\"Epoch [{epoch + 1}], Loss: {loss.item():.4f}, validation loss: {validation_losses[-1]:.4f}\"\n", + " )\n", + "\n", + " # store model state\n", + " model_states.append(model.state_dict())\n", + "\n", + " # Compute validation loss\n", + " with torch.no_grad():\n", + " outputs = model(val_features_normalized)\n", + " loss = criterion(outputs, validation_labels_tensor)\n", + " validation_losses.append(loss.item())\n", + "\n", + " # Check for early stopping if no improvement in validation loss in last 10 epochs\n", + " if epoch > 10 and validation_losses[-1] > validation_losses[-10]:\n", + " print(\"Early stopping\")\n", + " break\n", + "\n", + " epoch += 1\n", + "\n", + " best_model_state = model_states[np.argmin(validation_losses)]\n", + " model.load_state_dict(best_model_state)\n", + " validation_loss_of_best_model = validation_losses[np.argmin(validation_losses)]\n", + "\n", + " return model, epoch, validation_loss_of_best_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's create and train a model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Hyperparameters\n", + "input_dim = train_features_normalized.shape[1]\n", + "output_dim = len(set(train_labels)) # Assuming train_labels are class indices\n", + "hidden_dim = (input_dim + output_dim) // 2\n", + "\n", + "# Instantiate the model\n", + "model = SimpleNN(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train the model\n", + "model, epochs, val_loss = train(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "evaluate_model(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's prune the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def prune_pytorch_network(\n", + " model, weight_threshold=1e-1, bias_threshold=1e-1\n", + "): # noqa: D103\n", + " num_weights = 0\n", + " num_weights_already_zero = 0\n", + " num_changed_weights = 0\n", + " num_biases = 0\n", + " num_biases_already_zero = 0\n", + " num_changed_biases = 0\n", + "\n", + " # Pruning the weights\n", + " for name, param in model.named_parameters():\n", + " if \"weight\" in name:\n", + " flattened_weights = param.data.view(-1)\n", + " for j, weight in enumerate(flattened_weights):\n", + " if weight == 0:\n", + " num_weights_already_zero += 1\n", + " elif abs(weight) < weight_threshold:\n", + " flattened_weights[j] = 0\n", + " num_changed_weights += 1\n", + " num_weights += 1\n", + " param.data = flattened_weights.view(param.data.shape)\n", + " elif \"bias\" in name:\n", + " flattened_biases = param.data.view(-1)\n", + " for j, bias in enumerate(flattened_biases):\n", + " if bias == 0:\n", + " num_biases_already_zero += 1\n", + " elif abs(bias) < bias_threshold:\n", + " flattened_biases[j] = 0\n", + " num_changed_biases += 1\n", + " num_biases += 1\n", + " param.data = flattened_biases.view(param.data.shape)\n", + "\n", + " # Set gradients of pruned weights to zero\n", + " def zero_gradients_hook(grad):\n", + " return grad * (grad != 0)\n", + "\n", + " hooks = []\n", + " for name, param in model.named_parameters():\n", + " if \"weight\" in name or \"bias\" in name:\n", + " hooks.append(param.register_hook(zero_gradients_hook))\n", + "\n", + " print(f\"Number of weight parameters: {num_weights}\") # noqa: T201\n", + " print(f\"Number of weights already zero: {num_weights_already_zero}\") # noqa: T201\n", + " print(f\"Number of changed weight parameters: {num_changed_weights}\") # noqa: T201\n", + " print(f\"Number of bias parameters: {num_biases}\") # noqa: T201\n", + " print(f\"Number of biases already zero: {num_biases_already_zero}\") # noqa: T201\n", + " print(f\"Number of changed bias parameters: {num_changed_biases}\") # noqa: T201\n", + " print( # noqa: T201\n", + " f\"Percentage of weights pruned: {num_changed_weights / num_weights * 100:.2f}%\"\n", + " )\n", + " print( # noqa: T201\n", + " f\"Percentage of biases pruned: {num_changed_biases / num_biases * 100:.2f}%\"\n", + " )\n", + " print( # noqa: T201\n", + " f\"Remaining number of non-zero weights: {num_weights - num_changed_weights}\"\n", + " )\n", + " print( # noqa: T201\n", + " f\"Remaining number of non-zero biases: {num_biases - num_changed_biases}\"\n", + " )\n", + "\n", + " return model, hooks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "\n", + "model_pruned = copy.deepcopy(model)\n", + "model_pruned, hooks = prune_pytorch_network(model_pruned, 1e-1, 1e-1)\n", + "\n", + "_ = evaluate_model(model_pruned)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's have a loop of fine-tuning, pruning, and evaluating" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "val_loss_new = val_loss\n", + "model_to_prune = copy.deepcopy(model)\n", + "\n", + "while True:\n", + " print(f\"Iteration {i}\")\n", + " model_pruned = copy.deepcopy(model_to_prune)\n", + " model_pruned, _ = prune_pytorch_network(model_pruned, 1e-1, 1e-1)\n", + " val_loss_old = val_loss_new\n", + "\n", + " model_to_prune = copy.deepcopy(model_pruned)\n", + " print(\"Now training\")\n", + " model_to_prune, _, val_loss_new = train(model_to_prune)\n", + "\n", + " if val_loss_new > val_loss_old:\n", + " print(\"Early stopping\")\n", + " break\n", + "\n", + " i += 1\n", + "\n", + "accuracy = evaluate_model(model_pruned)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's convert this model to a scikit-learn MLP model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neural_network import MLPClassifier\n", + "\n", + "\n", + "def pytorch_to_sklearn(pytorch_model):\n", + "\n", + " # Extract weights and biases from PyTorch model\n", + " fc1_weight = pytorch_model.fc1.weight.data\n", + " fc1_bias = pytorch_model.fc1.bias.data\n", + " fc2_weight = pytorch_model.fc2.weight.data\n", + " fc2_bias = pytorch_model.fc2.bias.data\n", + "\n", + " # Get the sizes for initialization\n", + " input_size = fc1_weight.shape[1]\n", + " hidden_size = fc1_weight.shape[0]\n", + " output_size = fc2_weight.shape[0]\n", + "\n", + " # Initialize sklearn MLP\n", + " sklearn_mlp = MLPClassifier(\n", + " hidden_layer_sizes=(hidden_size,), activation=\"relu\", max_iter=1\n", + " )\n", + "\n", + " # To ensure the model doesn't change the weights during the dummy fit, we set warm_start=True\n", + " sklearn_mlp.warm_start = True\n", + "\n", + " # Dummy fit to initialize weights (necessary step before setting the weights)\n", + " sklearn_mlp.fit(np.zeros((output_size, input_size)), list(range(output_size)))\n", + "\n", + " # Set the weights and biases\n", + " sklearn_mlp.coefs_[0] = fc1_weight.t().numpy()\n", + " sklearn_mlp.intercepts_[0] = fc1_bias.numpy()\n", + " sklearn_mlp.coefs_[1] = fc2_weight.t().numpy()\n", + " sklearn_mlp.intercepts_[1] = fc2_bias.numpy()\n", + "\n", + " return sklearn_mlp\n", + "\n", + "\n", + "# Convert the example PyTorch MLP to sklearn MLP\n", + "converted_model = pytorch_to_sklearn(model_pruned)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate the sklearn model\n", + "accuracy = converted_model.score(test_features_normalized.numpy(), test_labels)\n", + "print(\"Accuracy:\", accuracy)\n", + "\n", + "layers_sizes = [converted_model.coefs_[0].shape[0]] + [\n", + " coef.shape[1] for coef in converted_model.coefs_\n", + "]\n", + "\n", + "print(\"Number of neurons per layer:\", layers_sizes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's transpile this model to Leo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from zkml import LeoTranspiler\n", + "\n", + "# Transpile the deceision tree into Leo code\n", + "print(type(converted_model))\n", + "lt = LeoTranspiler(\n", + " model=converted_model, validation_data=train_features_normalized[0:600].numpy()\n", + ")\n", + "leo_project_path = os.path.join(os.getcwd(), \"/tmp/mnist\")\n", + "leo_project_name = \"sklearn_mlp_mnist_1\"\n", + "lt.to_leo(\n", + " path=leo_project_path, project_name=leo_project_name, fixed_point_scaling_factor=16\n", + ")\n", + "\n", + "# Compute the accuracy of the Leo program and the Python program on the test set\n", + "num_test_samples = len(test_features)\n", + "\n", + "# let's limit the number of test stamples to 10 to make the computation faster\n", + "num_test_samples = min(num_test_samples, 50)\n", + "\n", + "python_predictions = converted_model.predict(test_features)\n", + "\n", + "leo_predictions = np.zeros(num_test_samples)\n", + "for i in range(num_test_samples):\n", + " lc = lt.run(input=test_features[i])\n", + " leo_predictions[i] = np.argmax(lc.output_decimal)\n", + "\n", + "print(f\"Constraints: {lc.circuit_constraints}\")\n", + "print(f\"Runtime for one instance: {lc.runtime} seconds\")\n", + "\n", + "leo_accuracy = (\n", + " np.sum(leo_predictions[0:num_test_samples] == test_labels[0:num_test_samples])\n", + " / num_test_samples\n", + ")\n", + "python_accuracy = (\n", + " np.sum(python_predictions[0:num_test_samples] == test_labels[0:num_test_samples])\n", + " / num_test_samples\n", + ")\n", + "\n", + "print(f\"Leo accuracy: {100*leo_accuracy} %\")\n", + "print(f\"Python accuracy: {100*python_accuracy} %\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's generate a proof" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zkp = lt.execute(input=test_features[0])\n", + "\n", + "print(f\"Constraints: {zkp.circuit_constraints}\")\n", + "print(f\"Runtime for one instance: {zkp.runtime} seconds\\n\")\n", + "\n", + "print(f\"Leo prediction: {zkp.output_decimal}\")\n", + "print(f\"Python prediction: {python_predictions[0]}\")\n", + "print(f\"True label: {test_labels[0]}\\n\")\n", + "\n", + "print(f\"Proof: {zkp.proof}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Plot the image\")\n", + "plt.imshow(test_images_tensor[0].reshape(28, 28), cmap=\"gray\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a56a14337e9b82d660ba7e576f3821afe9422add Mon Sep 17 00:00:00 2001 From: Konstantin Pandl <16907747+kpandl@users.noreply.github.com> Date: Wed, 30 Jul 2025 17:25:05 +0200 Subject: [PATCH 02/31] cleanup --- zkml-research/KYA_face/mnist_dataset.ipynb | 2990 +------------------- 1 file changed, 143 insertions(+), 2847 deletions(-) diff --git a/zkml-research/KYA_face/mnist_dataset.ipynb b/zkml-research/KYA_face/mnist_dataset.ipynb index d0c3e09..0788033 100644 --- a/zkml-research/KYA_face/mnist_dataset.ipynb +++ b/zkml-research/KYA_face/mnist_dataset.ipynb @@ -22,39 +22,38 @@ "* Re-train and evaluate the model\n", "* Transpilation to Leo\n", "* Test execution of the Leo program\n", - "* Deployment of the Leo program" + "* Deployment of the Leo program\n", + "* Extension ideas\n", + "* Discussion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## TODO Ensure Leo and Python library installation\n", + "## Ensure Leo and Python library installation\n", "For this Jupyter notebook to run successfully, you need to ensure Leo and selected Python libraries are installed. If you haven't done already ..." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/homebrew/lib/python3.13/site-packages/face_recognition_models/__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " from pkg_resources import resource_filename\n" - ] - } - ], + "outputs": [], "source": [ + "from zkml import LeoTranspiler\n", "import os\n", "from PIL import Image\n", "import matplotlib.pyplot as plt\n", "from pathlib import Path\n", "import face_recognition\n", "import pandas as pd\n", - "import numpy as np" + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.preprocessing import StandardScaler\n", + "from helper import plot_mlp_architecture, summarize_mlp\n", + "import random" ] }, { @@ -70,17 +69,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current working directory: /Users/kp/dev/python-sdk3/zkml-research/KYA_face\n" - ] - } - ], + "outputs": [], "source": [ "cwd = os.getcwd()\n", "print(f\"Current working directory: {cwd}\")\n", @@ -92,17 +83,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found expected files: ['1.jpg', '2.jpg', '3.jpg']\n" - ] - } - ], + "outputs": [], "source": [ "files = sorted(f for f in os.listdir(positive_dir_path)\n", " if os.path.isfile(os.path.join(positive_dir_path, f)))\n", @@ -114,20 +97,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "image_path = os.path.join(positive_dir_path, '3.jpg')\n", "img = Image.open(image_path)\n", @@ -146,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -174,18 +146,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[warn] no face in 24_0_2_20170116174324623.jpg, skipping\n", - "[warn] no face in 6_0_0_20170110213600515.jpg, skipping\n" - ] - } - ], + "outputs": [], "source": [ "# also define negative_dir_path\n", "negative_dir_path = os.path.join(cwd, 'face_images', 'negative')\n", @@ -204,95 +167,38 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.head()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "indices = [9, 24, 29, 32]\n", + "\n", + "# Fetch those filenames\n", + "filenames = df.loc[indices, 'filename'].tolist()\n", + "\n", + "# create a 2×2 grid of subplots\n", + "fig, axes = plt.subplots(2, 2, figsize=(8, 8))\n", + "\n", + "for ax, fname in zip(axes.flatten(), filenames):\n", + " image_path = os.path.join(negative_dir_path, fname)\n", + " img = Image.open(image_path)\n", + " ax.imshow(img)\n", + " ax.axis('off')\n", + " ax.set_title(fname)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -302,81 +208,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training samples: 67 (pos=2, neg=65)\n", - " Testing samples: 34 (pos=1, neg=33)\n" - ] - }, - { - "data": { - "text/plain": [ - "( filename label\n", - " 0 1.jpg 1\n", - " 1 2.jpg 1\n", - " 2 58_0_3_20170119211659305.jpg 0\n", - " 3 18_1_0_20170109213623055.jpg 0\n", - " 4 32_0_1_20170113133956457.jpg 0\n", - " .. ... ...\n", - " 62 40_0_3_20170117154652454.jpg 0\n", - " 63 46_1_1_20170116223430582.jpg 0\n", - " 64 25_1_0_20170117142815331.jpg 0\n", - " 65 68_0_0_20170105173736735.jpg 0\n", - " 66 35_0_3_20170117154729846.jpg 0\n", - " \n", - " [67 rows x 2 columns],\n", - " filename label\n", - " 0 3.jpg 1\n", - " 1 40_1_2_20170116161916676.jpg 0\n", - " 2 30_0_1_20170113195438285.jpg 0\n", - " 3 87_0_0_20170111222120006.jpg 0\n", - " 4 26_0_1_20170113151702303.jpg 0\n", - " 5 54_0_0_20170104165859441.jpg 0\n", - " 6 54_1_0_20170117191133235.jpg 0\n", - " 7 42_0_0_20170112220250648.jpg 0\n", - " 8 31_1_1_20170112231608655.jpg 0\n", - " 9 23_0_3_20170119164041958.jpg 0\n", - " 10 12_0_0_20170110215739155.jpg 0\n", - " 11 28_1_0_20170117180704448.jpg 0\n", - " 12 50_0_2_20170116191755793.jpg 0\n", - " 13 32_1_1_20170113011625824.jpg 0\n", - " 14 27_1_3_20170104223343215.jpg 0\n", - " 15 32_0_0_20170117140244970.jpg 0\n", - " 16 17_1_4_20170104001810179.jpg 0\n", - " 17 26_1_2_20170116182615565.jpg 0\n", - " 18 24_1_2_20170116174523538.jpg 0\n", - " 19 60_1_0_20170110154325940.jpg 0\n", - " 20 47_0_1_20170117021247110.jpg 0\n", - " 21 34_0_0_20170117105018453.jpg 0\n", - " 22 46_0_0_20170117190143691.jpg 0\n", - " 23 44_0_3_20170119200511259.jpg 0\n", - " 24 25_1_0_20170119172029833.jpg 0\n", - " 25 29_1_1_20170117105207326.jpg 0\n", - " 26 28_1_2_20170105000553851.jpg 0\n", - " 27 23_0_1_20170117194052028.jpg 0\n", - " 28 53_1_3_20170119205926527.jpg 0\n", - " 29 24_0_3_20170119164638750.jpg 0\n", - " 30 42_0_2_20170104192842031.jpg 0\n", - " 31 80_1_0_20170110131934630.jpg 0\n", - " 32 28_1_0_20170116222126409.jpg 0\n", - " 33 28_0_0_20170116212026440.jpg 0)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "from sklearn.neural_network import MLPClassifier\n", - "\n", "# 1) Positive class: first two → train, last one → test\n", "df_pos = df[df['label'] == 1].reset_index(drop=True)\n", "train_pos = df_pos.iloc[[0, 1]] # 1.jpg, 2.jpg\n", @@ -404,28 +239,15 @@ "\n", "# 5) Quick sanity check\n", "print(f\"Training samples: {len(train_df)} (pos={len(train_pos)}, neg={len(train_neg)})\")\n", - "print(f\" Testing samples: {len(test_df)} (pos={len(test_pos)}, neg={len(test_neg)})\")\n", - "train_df[['filename','label']], test_df[['filename','label']]" + "print(f\" Testing samples: {len(test_df)} (pos={len(test_pos)}, neg={len(test_neg)})\")" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train mean (first 5 features): [ 2.64092231e-17 5.30255773e-17 1.63012224e-16 -4.97114787e-17\n", - " -9.44518096e-17]\n", - "Train std (first 5 features): [1. 1. 1. 1. 1.]\n" - ] - } - ], + "outputs": [], "source": [ - "from sklearn.preprocessing import StandardScaler\n", - "\n", "# 1) Initialize scaler\n", "scaler = StandardScaler()\n", "\n", @@ -434,8 +256,8 @@ "X_test_scaled = scaler.transform(X_test)\n", "\n", "# 3) (Optional) check means/vars\n", - "print(\"Train mean (first 5 features):\", X_train_scaled.mean(axis=0)[:5])\n", - "print(\"Train std (first 5 features):\", X_train_scaled.std(axis=0)[:5])" + "print(\"Train mean (first 3 features):\", X_train_scaled.mean(axis=0)[:3])\n", + "print(\"Train std (first 3 features):\", X_train_scaled.std(axis=0)[:3])" ] }, { @@ -447,790 +269,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/homebrew/lib/python3.13/site-packages/sklearn/neural_network/_multilayer_perceptron.py:781: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
MLPClassifier(hidden_layer_sizes=(65,), max_iter=100, random_state=42)
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" - ], - "text/plain": [ - "MLPClassifier(hidden_layer_sizes=(65,), max_iter=100, random_state=42)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "clf_large = MLPClassifier(hidden_layer_sizes=(65,), max_iter=100, random_state=42)\n", "clf_large.fit(X_train_scaled, y_train)" @@ -1245,23 +286,32 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train accuracy: 1.0\n", - "Test accuracy: 1.0\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Train accuracy:\", clf_large.score(X_train_scaled, y_train))\n", "print(\"Test accuracy:\", clf_large.score(X_test_scaled, y_test))" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summarize_mlp(clf_large)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_mlp_architecture(clf_large)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1271,20 +321,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X_train_pca shape: (67, 32)\n", - "X_test_pca shape: (34, 32)\n", - "Cumulative explained variance (first 5 components): [0.13134312 0.25755996 0.33214079 0.39133033 0.44493415]\n", - "Total variance retained: 0.9277635063358127\n" - ] - } - ], + "outputs": [], "source": [ "# Cell X: PCA dimensionality reduction\n", "from sklearn.decomposition import PCA\n", @@ -1302,25 +341,14 @@ "\n", "# (Optional) Examine how much variance is retained\n", "explained = pca.explained_variance_ratio_.cumsum()\n", - "print(\"Cumulative explained variance (first 5 components):\", explained[:5])\n", "print(\"Total variance retained:\", explained[-1])" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Post-PCA train means (first 5 comps): [-4.97114787e-18 1.65704929e-17 -6.62819716e-18 -2.89983626e-17\n", - " 3.31409858e-18]\n", - "Post-PCA train stds (first 5 comps): [1. 1. 1. 1. 1.]\n" - ] - } - ], + "outputs": [], "source": [ "# Cell Y: Re-standardize PCA outputs before model training\n", "from sklearn.preprocessing import StandardScaler\n", @@ -1333,10 +361,10 @@ "X_test_pca_scaled = scaler_pca.transform(X_test_pca)\n", "\n", "# 3) Quick sanity check: zero mean/unit var on train\n", - "print(\"Post-PCA train means (first 5 comps):\", \n", - " X_train_pca_scaled.mean(axis=0)[:5])\n", - "print(\"Post-PCA train stds (first 5 comps):\", \n", - " X_train_pca_scaled.std(axis=0)[:5])" + "print(\"Post-PCA train means (first 3 comps):\", \n", + " X_train_pca_scaled.mean(axis=0)[:3])\n", + "print(\"Post-PCA train stds (first 3 comps):\", \n", + " X_train_pca_scaled.std(axis=0)[:3])" ] }, { @@ -1348,790 +376,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/homebrew/lib/python3.13/site-packages/sklearn/neural_network/_multilayer_perceptron.py:781: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
MLPClassifier(hidden_layer_sizes=(17,), max_iter=100, random_state=42)
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" - ], - "text/plain": [ - "MLPClassifier(hidden_layer_sizes=(17,), max_iter=100, random_state=42)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "clf_small = MLPClassifier(hidden_layer_sizes=(17,), max_iter=100, random_state=42)\n", "clf_small.fit(X_train_pca_scaled, y_train)" @@ -2139,133 +386,21 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train accuracy: 0.9850746268656716\n", - "Test accuracy: 0.9117647058823529\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Train accuracy:\", clf_small.score(X_train_pca_scaled, y_train))\n", "print(\"Test accuracy:\", clf_small.score(X_test_pca_scaled, y_test))" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Transpilation to Leo" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from zkml import LeoTranspiler\n", - "\n", - "# Transpile the NN into Leo code\n", - "lt = LeoTranspiler(\n", - " model=clf_small\n", - ")\n", - "leo_project_path = os.path.join(os.getcwd(), \"/tmp/mnist\")\n", - "leo_project_name = \"sklearn_mlp_mnist_1\"\n", - "lt.to_leo(\n", - " path=leo_project_path, project_name=leo_project_name, fixed_point_scaling_factor=16\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:No validation_data passed to the transpiler, thus, no information available of dataset shape. Passed input sample for run is treated as a single data point\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "python_predictions [[0 1]]\n", - "proba_list [[0.43960105 0.53485759]]\n", - "decimal output [-0.27880859375, 0.108154296875]\n", - "output [-1142, 443]\n", - "input ['{ x0: -21i64, x1: 6i64 }', '{ x0: 13i64, x1: 24i64 }', '{ x0: 0i64, x1: -11i64 }', '{ x0: -7i64, x1: -18i64 }', '{ x0: 3i64, x1: -17i64 }', '{ x0: 12i64, x1: -15i64 }', '{ x0: -15i64, x1: 28i64 }', '{ x0: -1i64, x1: 2i64 }', '{ x0: 8i64, x1: 10i64 }', '{ x0: 29i64, x1: -2i64 }', '{ x0: 2i64, x1: 8i64 }', '{ x0: 9i64, x1: -8i64 }', '{ x0: 9i64, x1: 24i64 }', '{ x0: 2i64, x1: -5i64 }', '{ x0: -8i64, x1: 7i64 }', '{ x0: -15i64, x1: -8i64 }']\n", - "Sigmoid output: (0.4307458893450617, 0.5270122483693945)\n", - "Constraints: 269822\n", - "Runtime for one instance: 2.605588436126709 seconds\n", - "[1.]\n" - ] - } - ], - "source": [ - "#print(y_test[0])\n", - "#print(y_test[1])\n", - "#print(y_test[2])\n", - "\n", - "\n", - "# Compute the accuracy of the Leo program and the Python program on the test set\n", - "num_test_samples = len(X_test_pca_scaled)\n", - "\n", - "# let's limit the number of test stamples to 10 to make the computation faster\n", - "num_test_samples = min(num_test_samples, 1)\n", - "#test_features = X_test_pca[:num_test_samples]\n", - "test_features = X_test_pca_scaled[0:1]\n", - "\n", - "python_predictions = clf_small.predict(test_features)\n", - "\n", - "print(\"python_predictions\", python_predictions)\n", - "\n", - "proba_list = clf_small.predict_proba(test_features)\n", - "\n", - "print(\"proba_list\", proba_list)\n", - "\n", - "leo_predictions = np.zeros(num_test_samples)\n", - "for i in range(num_test_samples):\n", - " lc = lt.run(input=test_features[i])\n", - " print(\"decimal output\", lc.output_decimal)\n", - " print(\"output\", lc.output)\n", - " print(\"input\", lc.input)\n", - " # compute softmax probabilities\n", - " logits = lc.output_decimal # Leo → Python floats\n", - " sigmoid_probs_0 = 1/(1+np.exp(-logits[0])) # element-wise\n", - " sigmoid_probs_1 = 1/(1+np.exp(-logits[1])) # element-wise\n", - " print(\"Sigmoid output:\", (sigmoid_probs_0, sigmoid_probs_1))\n", - " leo_predictions[i] = np.argmax(lc.output_decimal)\n", - "\n", - "print(f\"Constraints: {lc.circuit_constraints}\")\n", - "print(f\"Runtime for one instance: {lc.runtime} seconds\")\n", - "\n", - "print(leo_predictions)" - ] - }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# ...\n", - "a = 5/0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download the dataset" + "summarize_mlp(clf_small)" ] }, { @@ -2274,42 +409,14 @@ "metadata": {}, "outputs": [], "source": [ - "# URLs and filenames\n", - "file_info = [\n", - " (\n", - " \"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\",\n", - " \"train-images-idx3-ubyte.gz\",\n", - " ),\n", - " (\n", - " \"http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\",\n", - " \"train-labels-idx1-ubyte.gz\",\n", - " ),\n", - " (\n", - " \"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\",\n", - " \"t10k-images-idx3-ubyte.gz\",\n", - " ),\n", - " (\n", - " \"http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\",\n", - " \"t10k-labels-idx1-ubyte.gz\",\n", - " ),\n", - "]\n", - "\n", - "folder_name = \"tmp/mnist\"\n", - "folder_path = os.path.join(os.getcwd(), folder_name)\n", - "\n", - "os.makedirs(folder_path, exist_ok=True) # Create folder if it doesn't exist\n", - "\n", - "# Download and extract each file\n", - "for url, file_name in file_info:\n", - " path_to_save = os.path.join(folder_path, file_name)\n", - " download_and_extract_dataset(url, path_to_save, folder_path)" + "plot_mlp_architecture(clf_small)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Define function to read the dataset" + "## Transpilation to Leo" ] }, { @@ -2318,245 +425,18 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "\n", - "\n", - "def read_idx3_ubyte_image_file(filename):\n", - " \"\"\"Read IDX3-ubyte formatted image data.\"\"\"\n", - " with open(filename, \"rb\") as f:\n", - " magic_num = int.from_bytes(f.read(4), byteorder=\"big\")\n", - " num_images = int.from_bytes(f.read(4), byteorder=\"big\")\n", - " num_rows = int.from_bytes(f.read(4), byteorder=\"big\")\n", - " num_cols = int.from_bytes(f.read(4), byteorder=\"big\")\n", - "\n", - " if magic_num != 2051:\n", - " raise ValueError(f\"Invalid magic number: {magic_num}\")\n", - "\n", - " images = np.zeros((num_images, num_rows, num_cols), dtype=np.uint8)\n", - "\n", - " for i in range(num_images):\n", - " for r in range(num_rows):\n", - " for c in range(num_cols):\n", - " pixel = int.from_bytes(f.read(1), byteorder=\"big\")\n", - " images[i, r, c] = pixel\n", - "\n", - " return images\n", - "\n", - "\n", - "def read_idx1_ubyte_label_file(filename):\n", - " \"\"\"Read IDX1-ubyte formatted label data.\"\"\"\n", - " with open(filename, \"rb\") as f:\n", - " magic_num = int.from_bytes(f.read(4), byteorder=\"big\")\n", - " num_labels = int.from_bytes(f.read(4), byteorder=\"big\")\n", + "# Get a random number for the program name generation between 0 and 10000\n", + "random_number = random.randint(0, 10000)\n", "\n", - " if magic_num != 2049:\n", - " raise ValueError(f\"Invalid magic number: {magic_num}\")\n", + "# Create a unique project name using the random number\n", + "leo_project_name = f\"workshop_kya_face_{random_number}\"\n", "\n", - " labels = np.zeros(num_labels, dtype=np.uint8)\n", - "\n", - " for i in range(num_labels):\n", - " labels[i] = int.from_bytes(f.read(1), byteorder=\"big\")\n", - "\n", - " return labels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Read the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "folder_path = os.path.join(\n", - " os.getcwd(), folder_name\n", - ") # Adjust this path to where you stored the files\n", - "\n", - "train_images = read_idx3_ubyte_image_file(\n", - " os.path.join(folder_path, \"train-images-idx3-ubyte\")\n", - ")\n", - "train_labels = read_idx1_ubyte_label_file(\n", - " os.path.join(folder_path, \"train-labels-idx1-ubyte\")\n", - ")\n", - "test_images = read_idx3_ubyte_image_file(\n", - " os.path.join(folder_path, \"t10k-images-idx3-ubyte\")\n", - ")\n", - "test_labels = read_idx1_ubyte_label_file(\n", - " os.path.join(folder_path, \"t10k-labels-idx1-ubyte\")\n", + "# Transpile the NN into Leo code\n", + "lt = LeoTranspiler(\n", + " model=clf_small\n", ")\n", - "\n", - "print(\n", - " f\"Shape of train_images: {train_images.shape}\"\n", - ") # Should output \"Shape of train_images: (60000, 28, 28)\"\n", - "print(\n", - " f\"Shape of train_labels: {train_labels.shape}\"\n", - ") # Should output \"Shape of train_labels: (60000,)\"\n", - "print(\n", - " f\"Shape of test_images: {test_images.shape}\"\n", - ") # Should output \"Shape of test_images: (10000, 28, 28)\"\n", - "print(\n", - " f\"Shape of test_labels: {test_labels.shape}\"\n", - ") # Should output \"Shape of test_labels: (10000,)\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convert the datasets to PyTorch tensors, and get a validation set\n", - "(We use PyTorch instead of sci-kit learn to train sparse neural networks with L1 regularization)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# convertto pytorch tensors\n", - "import torch\n", - "\n", - "train_images_tensor_initial = torch.from_numpy(train_images).float()\n", - "train_labels_tensor_initial = torch.from_numpy(train_labels).long()\n", - "test_images_tensor = torch.from_numpy(test_images).float()\n", - "test_labels_tensor = torch.from_numpy(test_labels).long()\n", - "\n", - "# seed the random number generator\n", - "torch.manual_seed(0)\n", - "\n", - "# shuffle the training dataset\n", - "indices = torch.randperm(train_images_tensor_initial.shape[0])\n", - "train_images_tensor_shuffled = train_images_tensor_initial[indices]\n", - "train_labels_tensor_shuffled = train_labels_tensor_initial[indices]\n", - "\n", - "# get a 10% validation set\n", - "validation_size = int(train_images_tensor_shuffled.shape[0] * 0.1)\n", - "validation_images_tensor = train_images_tensor_shuffled[:validation_size]\n", - "validation_labels_tensor = train_labels_tensor_shuffled[:validation_size]\n", - "train_images_tensor = train_images_tensor_shuffled[validation_size:]\n", - "train_labels_tensor = train_labels_tensor_shuffled[validation_size:]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Train labels tensor shape:\", train_labels_tensor.shape)\n", - "print(\"Validation labels tensor shape:\", validation_labels_tensor.shape)\n", - "print(\"Test labels tensor shape:\", test_labels_tensor.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract feature representations of the dataset\n", - "\n", - "(We transform the bounding box images to 12x12 images, defined by the new_size variable. There is a trade-off in circuit constraints and ML model accuracy. You can increase the image size which will lead to a higher accuracy at the cost of more circuit constraints and thus longer proving times.)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "def get_bounding_box(img):\n", - " \"\"\"\n", - " Extract the bounding box from an MNIST image.\n", - "\n", - " Args:\n", - " img (np.ndarray): 2D numpy array representing the MNIST image.\n", - "\n", - " Returns:\n", - " (np.ndarray): Cropped image with the bounding box.\n", - " \"\"\"\n", - "\n", - " # convert torch image to numpy array\n", - " img = img.numpy()\n", - "\n", - " # Find the rows and columns where the image has non-zero pixels\n", - " rows = np.any(img, axis=1)\n", - " cols = np.any(img, axis=0)\n", - "\n", - " # Find the first and last row and column indices where the image has non-zero pixels\n", - " rmin, rmax = np.where(rows)[0][[0, -1]]\n", - " cmin, cmax = np.where(cols)[0][[0, -1]]\n", - "\n", - " # Return the cropped image\n", - " return img[rmin : rmax + 1, cmin : cmax + 1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cv2\n", - "\n", - "\n", - "def get_resized_datasets(\n", - " train_images_tensor, validation_images_tensor, test_images_tensor, new_size\n", - "):\n", - " num_train = len(train_images_tensor)\n", - " num_test = len(test_images_tensor)\n", - " num_val = len(validation_images_tensor)\n", - "\n", - " train_images_tensor_resized = np.zeros((num_train, new_size**2))\n", - " validation_images_tensor_resized = np.zeros((num_val, new_size**2))\n", - " test_images_tensor_resized = np.zeros((num_test, new_size**2))\n", - "\n", - " for i in range(num_train):\n", - " cropped_image = get_bounding_box(train_images_tensor[i].reshape(28, 28))\n", - " cropped_image_uint8 = np.clip(cropped_image, 0, 255).astype(np.uint8)\n", - " resized_image = cv2.resize(\n", - " cropped_image_uint8, (new_size, new_size), interpolation=cv2.INTER_AREA\n", - " )\n", - " train_images_tensor_resized[i, :] = resized_image.flatten()\n", - "\n", - " for i in range(num_val):\n", - " cropped_image = get_bounding_box(validation_images_tensor[i].reshape(28, 28))\n", - " cropped_image_uint8 = np.clip(cropped_image, 0, 255).astype(np.uint8)\n", - " resized_image = cv2.resize(\n", - " cropped_image_uint8, (new_size, new_size), interpolation=cv2.INTER_AREA\n", - " )\n", - " validation_images_tensor_resized[i, :] = resized_image.flatten()\n", - "\n", - " for i in range(num_test):\n", - " cropped_image = get_bounding_box(test_images_tensor[i].reshape(28, 28))\n", - " cropped_image_uint8 = np.clip(cropped_image, 0, 255).astype(np.uint8)\n", - " resized_image = cv2.resize(\n", - " cropped_image_uint8, (new_size, new_size), interpolation=cv2.INTER_AREA\n", - " )\n", - " test_images_tensor_resized[i, :] = resized_image.flatten()\n", - "\n", - " return (\n", - " train_images_tensor_resized,\n", - " validation_images_tensor_resized,\n", - " test_images_tensor_resized,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "new_size = 12\n", - "train_images_resized, val_images_resized, test_images_resized = get_resized_datasets(\n", - " train_images_tensor, validation_images_tensor, test_images_tensor, new_size\n", + "lt.to_leo(\n", + " path=cwd, project_name=leo_project_name, fixed_point_scaling_factor=16\n", ")" ] }, @@ -2564,357 +444,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Let's compute the features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def compute_haar_features(image):\n", - " # raise value error if the image is not square\n", - " if image.shape[0] != image.shape[1]:\n", - " raise ValueError(\"The input image must be square.\")\n", - "\n", - " features = []\n", - "\n", - " # Sliding window\n", - " for i in range(0, image.shape[0], 3): # Slide vertically with a step of 3\n", - " for j in range(0, image.shape[0], 3): # Slide horizontally with a step of 3\n", - "\n", - " if i + 6 > image.shape[0] or j + 6 > image.shape[0]:\n", - " continue\n", - "\n", - " # Extract 6x6 window\n", - " window = image[i : i + 6, j : j + 6]\n", - "\n", - " # Horizontal feature\n", - " horizontal_feature_value = np.sum(window[0:3, :]) - np.sum(window[3:6, :])\n", - "\n", - " # Vertical feature\n", - " vertical_feature_value = np.sum(window[:, 0:3]) - np.sum(window[:, 3:6])\n", - "\n", - " features.append(horizontal_feature_value)\n", - " features.append(vertical_feature_value)\n", - "\n", - " return np.array(features)\n", - "\n", - "\n", - "def aspect_ratio(image, threshold=0.5):\n", - " # Threshold the image to create a binary representation\n", - " bin_image = image > threshold\n", - " # Find the bounding box\n", - " row_indices, col_indices = np.nonzero(bin_image)\n", - " max_row, min_row = np.max(row_indices), np.min(row_indices)\n", - " max_col, min_col = np.max(col_indices), np.min(col_indices)\n", - "\n", - " # Calculate the aspect ratio of the bounding box\n", - " width = max_col - min_col + 1\n", - " height = max_row - min_row + 1\n", - "\n", - " if height == 0: # To avoid division by zero\n", - " return 1.0\n", - "\n", - " return width / height\n", - "\n", - "\n", - "from scipy.ndimage import label\n", - "\n", - "\n", - "def num_regions_below_threshold(image, threshold=0.5):\n", - " # Threshold the image so that pixels below the threshold are set to 1\n", - " # and those above the threshold are set to 0.\n", - " bin_image = image < threshold\n", - "\n", - " # Use connected components labeling\n", - " labeled_array, num_features = label(bin_image)\n", - "\n", - " # Return the number of unique regions\n", - " # (subtracting 1 as one of the labels will be the background)\n", - " return num_features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# compute datasets\n", - "\n", - "num_train = len(train_images_tensor)\n", - "num_val = len(validation_images_tensor)\n", - "num_test = len(test_images_tensor)\n", - "\n", - "aspect_ratio_train = np.zeros(num_train)\n", - "aspect_ratio_val = np.zeros(num_val)\n", - "aspect_ratio_test = np.zeros(num_test)\n", - "\n", - "num_white_regions_train = np.zeros(num_train)\n", - "num_white_regions_val = np.zeros(num_val)\n", - "num_white_regions_test = np.zeros(num_test)\n", - "\n", - "haar_1 = compute_haar_features(train_images_resized[0].reshape(new_size, new_size))\n", - "len_haar_features = len(haar_1)\n", - "\n", - "haar_train = np.zeros((num_train, len_haar_features))\n", - "haar_val = np.zeros((num_val, len_haar_features))\n", - "haar_test = np.zeros((num_test, len_haar_features))\n", - "\n", - "for i in range(num_train):\n", - " aspect_ratio_train[i] = aspect_ratio(train_images_tensor[i].reshape(28, 28).numpy())\n", - " num_white_regions_train[i] = num_regions_below_threshold(\n", - " train_images_tensor[i].reshape(28, 28)\n", - " )\n", - " haar_train[i, :] = compute_haar_features(\n", - " train_images_resized[i, :].reshape(new_size, new_size)\n", - " )\n", - "\n", - "for i in range(num_val):\n", - " aspect_ratio_val[i] = aspect_ratio(\n", - " validation_images_tensor[i].reshape(28, 28).numpy()\n", - " )\n", - " num_white_regions_val[i] = num_regions_below_threshold(\n", - " validation_images_tensor[i].reshape(28, 28)\n", - " )\n", - " haar_val[i, :] = compute_haar_features(\n", - " val_images_resized[i, :].reshape(new_size, new_size)\n", - " )\n", - "\n", - "for i in range(num_test):\n", - " aspect_ratio_test[i] = aspect_ratio(test_images_tensor[i].reshape(28, 28).numpy())\n", - " num_white_regions_test[i] = num_regions_below_threshold(\n", - " test_images_tensor[i].reshape(28, 28)\n", - " )\n", - " haar_test[i, :] = compute_haar_features(\n", - " test_images_resized[i, :].reshape(new_size, new_size)\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Take a look at the images, and the computed features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "image_id = 0\n", - "\n", - "image = train_images_tensor[image_id].reshape(28, 28)\n", - "\n", - "print(\"Original image\")\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.imshow(image, cmap=\"gray\")\n", - "plt.show()\n", - "\n", - "print(\"Image shape\", image.shape)\n", - "print(\"Label\", train_labels_tensor[image_id])\n", - "\n", - "print(\"Resized image\")\n", - "\n", - "image_resized = train_images_resized[image_id].reshape(new_size, new_size)\n", - "\n", - "plt.imshow(image_resized, cmap=\"gray\")\n", - "plt.show()\n", - "\n", - "print(\"Image shape\", image_resized.shape)\n", - "\n", - "print(\"Haar features:\", haar_train[image_id, :])\n", - "print(\"Shape of Haar features:\", haar_train[image_id, :].shape)\n", - "\n", - "print(\"Aspect ratio:\", aspect_ratio_train[image_id])\n", - "print(\"Number of white regions:\", num_white_regions_train[image_id])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Let's merge all features into one dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# compute datasets\n", - "\n", - "train_features = np.zeros((num_train, len_haar_features + 2))\n", - "val_features = np.zeros((num_val, len_haar_features + 2))\n", - "test_features = np.zeros((num_test, len_haar_features + 2))\n", - "\n", - "for i in range(num_train):\n", - " train_features[i, :] = np.hstack(\n", - " (haar_train[i, :], aspect_ratio_train[i], num_white_regions_train[i])\n", - " )\n", - "\n", - "for i in range(num_val):\n", - " val_features[i, :] = np.hstack(\n", - " (haar_val[i, :], aspect_ratio_val[i], num_white_regions_val[i])\n", - " )\n", - "\n", - "for i in range(num_test):\n", - " test_features[i, :] = np.hstack(\n", - " (haar_test[i, :], aspect_ratio_test[i], num_white_regions_test[i])\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Shape of training features:\", train_features.shape)\n", - "print(\"First training feature vector:\", train_features[0, :])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Normalize the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "scaler = StandardScaler()\n", - "train_features_normalized = torch.tensor(scaler.fit_transform(train_features)).float()\n", - "val_features_normalized = torch.tensor(scaler.transform(val_features)).float()\n", - "test_features_normalized = torch.tensor(scaler.transform(test_features)).float()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"First training feature vector (normalized):\", train_features_normalized[0, :])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define the neural network and the training and test function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "\n", - "def evaluate_model(model):\n", - " model.eval() # Set the model to evaluation mode\n", - " with torch.no_grad():\n", - " test_outputs = model(test_features_normalized)\n", - " _, predicted = torch.max(test_outputs.data, 1)\n", - " accuracy = accuracy_score(test_labels, predicted.numpy())\n", - " print(\"Accuracy:\", accuracy)\n", - " return accuracy\n", - "\n", - "\n", - "# Define the PyTorch neural network\n", - "class SimpleNN(nn.Module):\n", - " def __init__(self, input_dim, hidden_dim, output_dim):\n", - " super(SimpleNN, self).__init__()\n", - " self.fc1 = nn.Linear(input_dim, hidden_dim)\n", - " self.fc2 = nn.Linear(hidden_dim, output_dim)\n", - "\n", - " def forward(self, x):\n", - " x = torch.relu(self.fc1(x))\n", - " x = self.fc2(x)\n", - " return x\n", - "\n", - "\n", - "def train(model):\n", - " model.train()\n", - " criterion = nn.CrossEntropyLoss()\n", - " optimizer = optim.Adam(model.parameters(), lr=0.001)\n", - "\n", - " # Training loop with L1 regularization\n", - " lambda_l1 = 0.0001 # L1 regularization coefficient\n", - "\n", - " validation_losses = []\n", - " epoch = 0\n", - "\n", - " model_states = []\n", - "\n", - " while True:\n", - " optimizer.zero_grad()\n", - " outputs = model(train_features_normalized)\n", - "\n", - " loss = criterion(outputs, train_labels_tensor)\n", - "\n", - " # Add L1 regularization\n", - " l1_reg = torch.tensor(0.0, requires_grad=True)\n", - " for param in model.parameters():\n", - " l1_reg = l1_reg + torch.norm(param, 1)\n", - " loss += lambda_l1 * l1_reg\n", - "\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # Print progress\n", - " if (epoch + 1) % 100 == 0:\n", - " print(\n", - " f\"Epoch [{epoch + 1}], Loss: {loss.item():.4f}, validation loss: {validation_losses[-1]:.4f}\"\n", - " )\n", - "\n", - " # store model state\n", - " model_states.append(model.state_dict())\n", - "\n", - " # Compute validation loss\n", - " with torch.no_grad():\n", - " outputs = model(val_features_normalized)\n", - " loss = criterion(outputs, validation_labels_tensor)\n", - " validation_losses.append(loss.item())\n", - "\n", - " # Check for early stopping if no improvement in validation loss in last 10 epochs\n", - " if epoch > 10 and validation_losses[-1] > validation_losses[-10]:\n", - " print(\"Early stopping\")\n", - " break\n", - "\n", - " epoch += 1\n", - "\n", - " best_model_state = model_states[np.argmin(validation_losses)]\n", - " model.load_state_dict(best_model_state)\n", - " validation_loss_of_best_model = validation_losses[np.argmin(validation_losses)]\n", - "\n", - " return model, epoch, validation_loss_of_best_model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Let's create and train a model" + "## Test execution of the Leo program" ] }, { @@ -2923,130 +453,38 @@ "metadata": {}, "outputs": [], "source": [ - "# Hyperparameters\n", - "input_dim = train_features_normalized.shape[1]\n", - "output_dim = len(set(train_labels)) # Assuming train_labels are class indices\n", - "hidden_dim = (input_dim + output_dim) // 2\n", + "def sigmoid(x):\n", + " return 1 / (1 + np.exp(-x))\n", "\n", - "# Instantiate the model\n", - "model = SimpleNN(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train the model\n", - "model, epochs, val_loss = train(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "evaluate_model(model)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Let's prune the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def prune_pytorch_network(\n", - " model, weight_threshold=1e-1, bias_threshold=1e-1\n", - "): # noqa: D103\n", - " num_weights = 0\n", - " num_weights_already_zero = 0\n", - " num_changed_weights = 0\n", - " num_biases = 0\n", - " num_biases_already_zero = 0\n", - " num_changed_biases = 0\n", + "# pick out the first test vector\n", + "test_features = X_test_pca_scaled[0:1]\n", "\n", - " # Pruning the weights\n", - " for name, param in model.named_parameters():\n", - " if \"weight\" in name:\n", - " flattened_weights = param.data.view(-1)\n", - " for j, weight in enumerate(flattened_weights):\n", - " if weight == 0:\n", - " num_weights_already_zero += 1\n", - " elif abs(weight) < weight_threshold:\n", - " flattened_weights[j] = 0\n", - " num_changed_weights += 1\n", - " num_weights += 1\n", - " param.data = flattened_weights.view(param.data.shape)\n", - " elif \"bias\" in name:\n", - " flattened_biases = param.data.view(-1)\n", - " for j, bias in enumerate(flattened_biases):\n", - " if bias == 0:\n", - " num_biases_already_zero += 1\n", - " elif abs(bias) < bias_threshold:\n", - " flattened_biases[j] = 0\n", - " num_changed_biases += 1\n", - " num_biases += 1\n", - " param.data = flattened_biases.view(param.data.shape)\n", + "# sklearn’s probability\n", + "python_probability_prediction = clf_small.predict_proba(test_features)\n", + "print(f\"Python probability prediction: {python_probability_prediction}\\n\")\n", "\n", - " # Set gradients of pruned weights to zero\n", - " def zero_gradients_hook(grad):\n", - " return grad * (grad != 0)\n", + "# Leo computation\n", + "leo_computation = lt.run(input=test_features[0])\n", "\n", - " hooks = []\n", - " for name, param in model.named_parameters():\n", - " if \"weight\" in name or \"bias\" in name:\n", - " hooks.append(param.register_hook(zero_gradients_hook))\n", + "print(\"Leo computation results\")\n", + "print(f\"\\tConstraints: {leo_computation.circuit_constraints}\")\n", + "print(f\"\\tRuntime: {leo_computation.runtime} seconds\")\n", + "print(f\"\\tInput: {leo_computation.input}\")\n", + "print(f\"\\tRaw output: {leo_computation.output}\")\n", + "print(f\"\\tDecimal output: {leo_computation.output_decimal}\")\n", "\n", - " print(f\"Number of weight parameters: {num_weights}\") # noqa: T201\n", - " print(f\"Number of weights already zero: {num_weights_already_zero}\") # noqa: T201\n", - " print(f\"Number of changed weight parameters: {num_changed_weights}\") # noqa: T201\n", - " print(f\"Number of bias parameters: {num_biases}\") # noqa: T201\n", - " print(f\"Number of biases already zero: {num_biases_already_zero}\") # noqa: T201\n", - " print(f\"Number of changed bias parameters: {num_changed_biases}\") # noqa: T201\n", - " print( # noqa: T201\n", - " f\"Percentage of weights pruned: {num_changed_weights / num_weights * 100:.2f}%\"\n", - " )\n", - " print( # noqa: T201\n", - " f\"Percentage of biases pruned: {num_changed_biases / num_biases * 100:.2f}%\"\n", - " )\n", - " print( # noqa: T201\n", - " f\"Remaining number of non-zero weights: {num_weights - num_changed_weights}\"\n", - " )\n", - " print( # noqa: T201\n", - " f\"Remaining number of non-zero biases: {num_biases - num_changed_biases}\"\n", - " )\n", + "# Convert list → array for sigmoid\n", + "decimal_array = np.array(leo_computation.output_decimal, dtype=float)\n", + "leo_probs = sigmoid(decimal_array)\n", "\n", - " return model, hooks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "\n", - "model_pruned = copy.deepcopy(model)\n", - "model_pruned, hooks = prune_pytorch_network(model_pruned, 1e-1, 1e-1)\n", - "\n", - "_ = evaluate_model(model_pruned)" + "print(f\"\\tProbabilities after sigmoid: {leo_probs.tolist()}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Let's have a loop of fine-tuning, pruning, and evaluating" + "## Deployment of the Leo program" ] }, { @@ -3055,188 +493,46 @@ "metadata": {}, "outputs": [], "source": [ - "i = 0\n", - "val_loss_new = val_loss\n", - "model_to_prune = copy.deepcopy(model)\n", - "\n", - "while True:\n", - " print(f\"Iteration {i}\")\n", - " model_pruned = copy.deepcopy(model_to_prune)\n", - " model_pruned, _ = prune_pytorch_network(model_pruned, 1e-1, 1e-1)\n", - " val_loss_old = val_loss_new\n", - "\n", - " model_to_prune = copy.deepcopy(model_pruned)\n", - " print(\"Now training\")\n", - " model_to_prune, _, val_loss_new = train(model_to_prune)\n", - "\n", - " if val_loss_new > val_loss_old:\n", - " print(\"Early stopping\")\n", - " break\n", - "\n", - " i += 1\n", - "\n", - "accuracy = evaluate_model(model_pruned)" + "print(os.path.join(cwd, leo_project_name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Let's convert this model to a scikit-learn MLP model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.neural_network import MLPClassifier\n", - "\n", - "\n", - "def pytorch_to_sklearn(pytorch_model):\n", - "\n", - " # Extract weights and biases from PyTorch model\n", - " fc1_weight = pytorch_model.fc1.weight.data\n", - " fc1_bias = pytorch_model.fc1.bias.data\n", - " fc2_weight = pytorch_model.fc2.weight.data\n", - " fc2_bias = pytorch_model.fc2.bias.data\n", - "\n", - " # Get the sizes for initialization\n", - " input_size = fc1_weight.shape[1]\n", - " hidden_size = fc1_weight.shape[0]\n", - " output_size = fc2_weight.shape[0]\n", - "\n", - " # Initialize sklearn MLP\n", - " sklearn_mlp = MLPClassifier(\n", - " hidden_layer_sizes=(hidden_size,), activation=\"relu\", max_iter=1\n", - " )\n", - "\n", - " # To ensure the model doesn't change the weights during the dummy fit, we set warm_start=True\n", - " sklearn_mlp.warm_start = True\n", - "\n", - " # Dummy fit to initialize weights (necessary step before setting the weights)\n", - " sklearn_mlp.fit(np.zeros((output_size, input_size)), list(range(output_size)))\n", - "\n", - " # Set the weights and biases\n", - " sklearn_mlp.coefs_[0] = fc1_weight.t().numpy()\n", - " sklearn_mlp.intercepts_[0] = fc1_bias.numpy()\n", - " sklearn_mlp.coefs_[1] = fc2_weight.t().numpy()\n", - " sklearn_mlp.intercepts_[1] = fc2_bias.numpy()\n", - "\n", - " return sklearn_mlp\n", - "\n", - "\n", - "# Convert the example PyTorch MLP to sklearn MLP\n", - "converted_model = pytorch_to_sklearn(model_pruned)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Evaluate the sklearn model\n", - "accuracy = converted_model.score(test_features_normalized.numpy(), test_labels)\n", - "print(\"Accuracy:\", accuracy)\n", - "\n", - "layers_sizes = [converted_model.coefs_[0].shape[0]] + [\n", - " coef.shape[1] for coef in converted_model.coefs_\n", - "]\n", - "\n", - "print(\"Number of neurons per layer:\", layers_sizes)" + "Open the directory above below, update the private key with a funded one in the `.env` file and run `leo deploy --broadcast`. Then, check for the transaction to land in the [Provable testnet explorer](https://testnet.explorer.provable.com/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Let's transpile this model to Leo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from zkml import LeoTranspiler\n", + "## Extension ideas\n", + "### Machine Learning\n", + "* Add data augmentation\n", + "* Add early stopping and validation approach in training\n", + "* Explore data quantization and other ML models\n", + "* ...\n", "\n", - "# Transpile the deceision tree into Leo code\n", - "print(type(converted_model))\n", - "lt = LeoTranspiler(\n", - " model=converted_model, validation_data=train_features_normalized[0:600].numpy()\n", - ")\n", - "leo_project_path = os.path.join(os.getcwd(), \"/tmp/mnist\")\n", - "leo_project_name = \"sklearn_mlp_mnist_1\"\n", - "lt.to_leo(\n", - " path=leo_project_path, project_name=leo_project_name, fixed_point_scaling_factor=16\n", - ")\n", - "\n", - "# Compute the accuracy of the Leo program and the Python program on the test set\n", - "num_test_samples = len(test_features)\n", - "\n", - "# let's limit the number of test stamples to 10 to make the computation faster\n", - "num_test_samples = min(num_test_samples, 50)\n", - "\n", - "python_predictions = converted_model.predict(test_features)\n", - "\n", - "leo_predictions = np.zeros(num_test_samples)\n", - "for i in range(num_test_samples):\n", - " lc = lt.run(input=test_features[i])\n", - " leo_predictions[i] = np.argmax(lc.output_decimal)\n", - "\n", - "print(f\"Constraints: {lc.circuit_constraints}\")\n", - "print(f\"Runtime for one instance: {lc.runtime} seconds\")\n", + "### ZK\n", + "* ML model parameters as private inputs (so no one can steal your model)\n", + "* Hash model parameters to commit to an idendity\n", + "* Store result on-chain\n", + "* ...\n", "\n", - "leo_accuracy = (\n", - " np.sum(leo_predictions[0:num_test_samples] == test_labels[0:num_test_samples])\n", - " / num_test_samples\n", - ")\n", - "python_accuracy = (\n", - " np.sum(python_predictions[0:num_test_samples] == test_labels[0:num_test_samples])\n", - " / num_test_samples\n", - ")\n", - "\n", - "print(f\"Leo accuracy: {100*leo_accuracy} %\")\n", - "print(f\"Python accuracy: {100*python_accuracy} %\")" + "Check out the [Provable KYA app](https://kya.provable.com/) where some of these ideas are already implemented!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Let's generate a proof" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "zkp = lt.execute(input=test_features[0])\n", - "\n", - "print(f\"Constraints: {zkp.circuit_constraints}\")\n", - "print(f\"Runtime for one instance: {zkp.runtime} seconds\\n\")\n", - "\n", - "print(f\"Leo prediction: {zkp.output_decimal}\")\n", - "print(f\"Python prediction: {python_predictions[0]}\")\n", - "print(f\"True label: {test_labels[0]}\\n\")\n", - "\n", - "print(f\"Proof: {zkp.proof}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Plot the image\")\n", - "plt.imshow(test_images_tensor[0].reshape(28, 28), cmap=\"gray\")\n", - "plt.show()" + "## Discussion\n", + "Wa are interested in a broad discussion with you, including the following aspects:\n", + "* Applications\n", + "* Security\n", + "* Technical improvements\n", + "* ..." ] } ], From 34286a7e8d365ab52bff161630a0526664120767 Mon Sep 17 00:00:00 2001 From: Konstantin Pandl <16907747+kpandl@users.noreply.github.com> Date: Wed, 30 Jul 2025 17:56:27 +0200 Subject: [PATCH 03/31] further cleanup --- zkml-research/KYA_face/mnist_dataset.ipynb | 105 ++++++++++++++++----- 1 file changed, 79 insertions(+), 26 deletions(-) diff --git a/zkml-research/KYA_face/mnist_dataset.ipynb b/zkml-research/KYA_face/mnist_dataset.ipynb index 0788033..5ad0658 100644 --- a/zkml-research/KYA_face/mnist_dataset.ipynb +++ b/zkml-research/KYA_face/mnist_dataset.ipynb @@ -61,8 +61,8 @@ "metadata": {}, "source": [ "## Add your own image (optional)\n", - "In this notebook, we use 3 JPG images of Albert Einstein as the positive class.\n", - "You can replace these with 3 JPG images of yourself - place the files `1.jpg`, `2.jpg`, `3.jpg` in the folder.\n", + "In this notebook, we use 5 JPG images of Albert Einstein as the positive class.\n", + "You can replace these with 5 JPG images of yourself - place the files `1.jpg`, `2.jpg`, `3.jpg`, `4.jpg`, `5.jpg` in the folder.\n", "\n", "The following code ensures the files inside the positive class folder are correctly set up." ] @@ -89,7 +89,7 @@ "source": [ "files = sorted(f for f in os.listdir(positive_dir_path)\n", " if os.path.isfile(os.path.join(positive_dir_path, f)))\n", - "expected = ['1.jpg', '2.jpg', '3.jpg']\n", + "expected = ['1.jpg', '2.jpg', '3.jpg', '4.jpg', '5.jpg']\n", "if files != expected:\n", " raise ValueError(f\"Expected {expected}, but found {files}\")\n", "print(f\"Found expected files: {files}\")" @@ -101,11 +101,11 @@ "metadata": {}, "outputs": [], "source": [ - "image_path = os.path.join(positive_dir_path, '3.jpg')\n", + "image_path = os.path.join(positive_dir_path, '4.jpg')\n", "img = Image.open(image_path)\n", "plt.imshow(img)\n", "plt.axis('off')\n", - "plt.title('3.jpg')\n", + "plt.title('4.jpg')\n", "plt.show()" ] }, @@ -214,8 +214,8 @@ "source": [ "# 1) Positive class: first two → train, last one → test\n", "df_pos = df[df['label'] == 1].reset_index(drop=True)\n", - "train_pos = df_pos.iloc[[0, 1]] # 1.jpg, 2.jpg\n", - "test_pos = df_pos.iloc[[2]] # 3.jpg\n", + "train_pos = df_pos.iloc[[0, 1, 2]] # 1.jpg, 2.jpg\n", + "test_pos = df_pos.iloc[[3, 4]] # 3.jpg\n", "\n", "# 2) Negative class: random 2/3 train, 1/3 test (seed=42)\n", "df_neg = df[df['label'] == 0].reset_index(drop=True)\n", @@ -454,37 +454,90 @@ "outputs": [], "source": [ "def sigmoid(x):\n", - " return 1 / (1 + np.exp(-x))\n", + " return 1 / (1 + np.exp(-x))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def show_image_and_predictions(test_index):\n", + " test_features = X_test_pca_scaled[test_index:test_index+1]\n", "\n", - "# pick out the first test vector\n", - "test_features = X_test_pca_scaled[0:1]\n", + " sample_meta = test_df.iloc[test_index] # row that produced X_test[test_index]\n", + " img_dir = positive_dir_path if sample_meta['label'] == 1 else negative_dir_path\n", + " image_path = os.path.join(img_dir, sample_meta['filename'])\n", + "\n", + " # todo I want to use the same image as in the prediction below. what do I need to do?\n", + " img = Image.open(image_path)\n", + " plt.figure(figsize=(3, 3)) \n", + " plt.imshow(img)\n", + " plt.axis('off')\n", + " plt.title(sample_meta['filename'])\n", + " plt.show()\n", "\n", - "# sklearn’s probability\n", - "python_probability_prediction = clf_small.predict_proba(test_features)\n", - "print(f\"Python probability prediction: {python_probability_prediction}\\n\")\n", + " # sklearn’s probability\n", + " python_probability_prediction = clf_small.predict_proba(test_features)\n", + " print(f\"Python probability prediction: {python_probability_prediction}\\n\")\n", "\n", - "# Leo computation\n", - "leo_computation = lt.run(input=test_features[0])\n", + " # Leo computation\n", + " leo_computation = lt.run(input=test_features[0])\n", "\n", - "print(\"Leo computation results\")\n", - "print(f\"\\tConstraints: {leo_computation.circuit_constraints}\")\n", - "print(f\"\\tRuntime: {leo_computation.runtime} seconds\")\n", - "print(f\"\\tInput: {leo_computation.input}\")\n", - "print(f\"\\tRaw output: {leo_computation.output}\")\n", - "print(f\"\\tDecimal output: {leo_computation.output_decimal}\")\n", + " print(\"Leo computation results\")\n", + " print(f\"\\tConstraints: {leo_computation.circuit_constraints}\")\n", + " print(f\"\\tRuntime: {leo_computation.runtime} seconds\")\n", + " print(f\"\\tInput: {leo_computation.input}\")\n", + " print(f\"\\tRaw output: {leo_computation.output}\")\n", + " print(f\"\\tDecimal output: {leo_computation.output_decimal}\")\n", "\n", - "# Convert list → array for sigmoid\n", - "decimal_array = np.array(leo_computation.output_decimal, dtype=float)\n", - "leo_probs = sigmoid(decimal_array)\n", + " # Convert list → array for sigmoid\n", + " decimal_array = np.array(leo_computation.output_decimal, dtype=float)\n", + " leo_probs = sigmoid(decimal_array)\n", "\n", - "print(f\"\\tProbabilities after sigmoid: {leo_probs.tolist()}\")" + " print(f\"\\tProbabilities after sigmoid: {leo_probs.tolist()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_image_and_predictions(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_image_and_predictions(1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_image_and_predictions(13)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: the two probabilities need not sum to 1 because each class is modelled with an independent sigmoid, not a shared soft-max." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Deployment of the Leo program" + "## Deploy the Leo program" ] }, { From 87b7b2a7e5deb82f06d921d4f637201c36bc42b1 Mon Sep 17 00:00:00 2001 From: Konstantin Pandl <16907747+kpandl@users.noreply.github.com> Date: Wed, 30 Jul 2025 18:19:35 +0200 Subject: [PATCH 04/31] cleanup --- zkml-research/KYA_face/mnist_dataset.ipynb | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/zkml-research/KYA_face/mnist_dataset.ipynb b/zkml-research/KYA_face/mnist_dataset.ipynb index 5ad0658..de70ac7 100644 --- a/zkml-research/KYA_face/mnist_dataset.ipynb +++ b/zkml-research/KYA_face/mnist_dataset.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Demo of KYA face identification - MLP neural network training and Leo transpilation" + "# KYA face identification demo - MLP neural network training, Leo transpilation, Aleo deployment" ] }, { @@ -32,7 +32,7 @@ "metadata": {}, "source": [ "## Ensure Leo and Python library installation\n", - "For this Jupyter notebook to run successfully, you need to ensure Leo and selected Python libraries are installed. If you haven't done already ..." + "For this Jupyter notebook to run successfully, you need to ensure Leo and selected Python libraries are installed. If you haven't done already ... (todo)" ] }, { @@ -466,11 +466,11 @@ "def show_image_and_predictions(test_index):\n", " test_features = X_test_pca_scaled[test_index:test_index+1]\n", "\n", - " sample_meta = test_df.iloc[test_index] # row that produced X_test[test_index]\n", + " sample_meta = test_df.iloc[test_index]\n", " img_dir = positive_dir_path if sample_meta['label'] == 1 else negative_dir_path\n", " image_path = os.path.join(img_dir, sample_meta['filename'])\n", "\n", - " # todo I want to use the same image as in the prediction below. what do I need to do?\n", + " # plot image\n", " img = Image.open(image_path)\n", " plt.figure(figsize=(3, 3)) \n", " plt.imshow(img)\n", @@ -480,7 +480,8 @@ "\n", " # sklearn’s probability\n", " python_probability_prediction = clf_small.predict_proba(test_features)\n", - " print(f\"Python probability prediction: {python_probability_prediction}\\n\")\n", + " print(f\"Python probability prediction: {python_probability_prediction}\")\n", + " print(f\"Predicted class: {np.argmax(python_probability_prediction)}\\n\")\n", "\n", " # Leo computation\n", " leo_computation = lt.run(input=test_features[0])\n", @@ -496,7 +497,8 @@ " decimal_array = np.array(leo_computation.output_decimal, dtype=float)\n", " leo_probs = sigmoid(decimal_array)\n", "\n", - " print(f\"\\tProbabilities after sigmoid: {leo_probs.tolist()}\")" + " print(f\"\\tProbabilities after sigmoid: {leo_probs.tolist()}\")\n", + " print(f\"\\tPredicted class: {np.argmax(leo_probs)}\")" ] }, { @@ -530,7 +532,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note: the two probabilities need not sum to 1 because each class is modelled with an independent sigmoid, not a shared soft-max." + "Note:\n", + "* the two probabilities need not sum to 1 because each class is modelled with an independent sigmoid, not a shared soft-max.\n", + "* small deviations in the probabilities between the Python and the Leo variant come from the fixed point number representation." ] }, { @@ -553,7 +557,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Open the directory above below, update the private key with a funded one in the `.env` file and run `leo deploy --broadcast`. Then, check for the transaction to land in the [Provable testnet explorer](https://testnet.explorer.provable.com/)." + "Open the directory above, update the private key with a funded one in the `.env` file and run `leo deploy --broadcast`. Then, check for the transaction to land in the [Provable testnet explorer](https://testnet.explorer.provable.com/)." ] }, { @@ -569,7 +573,7 @@ "\n", "### ZK\n", "* ML model parameters as private inputs (so no one can steal your model)\n", - "* Hash model parameters to commit to an idendity\n", + "* Hash model parameters to commit to an identity\n", "* Store result on-chain\n", "* ...\n", "\n", From e15ffe66df57ea142266a100f6b9a798ef263319 Mon Sep 17 00:00:00 2001 From: Konstantin Pandl <16907747+kpandl@users.noreply.github.com> Date: Wed, 30 Jul 2025 18:42:21 +0200 Subject: [PATCH 05/31] ease installation --- zkml-research/KYA_face/.gitignore | 2 + zkml-research/KYA_face/README.md | 8 ++++ zkml-research/KYA_face/helper.py | 63 +++++++++++++++++++++++++ zkml-research/KYA_face/requirements.txt | 13 +++++ 4 files changed, 86 insertions(+) create mode 100644 zkml-research/KYA_face/.gitignore create mode 100644 zkml-research/KYA_face/README.md create mode 100644 zkml-research/KYA_face/helper.py create mode 100644 zkml-research/KYA_face/requirements.txt diff --git a/zkml-research/KYA_face/.gitignore b/zkml-research/KYA_face/.gitignore new file mode 100644 index 0000000..a9c9101 --- /dev/null +++ b/zkml-research/KYA_face/.gitignore @@ -0,0 +1,2 @@ +.venv/ +workshop_* \ No newline at end of file diff --git a/zkml-research/KYA_face/README.md b/zkml-research/KYA_face/README.md new file mode 100644 index 0000000..14a3b3c --- /dev/null +++ b/zkml-research/KYA_face/README.md @@ -0,0 +1,8 @@ +# KYA workshop + +Steps to setup: +* Create & activate a virtual env called .venv `python -m venv .venv` +* Activate it + * Windows: `.venv\Scripts\activate` + * macOS/Linux: `source .venv/bin/activate` +* install the required Python libraries: `pip install -r requirements.txt` \ No newline at end of file diff --git a/zkml-research/KYA_face/helper.py b/zkml-research/KYA_face/helper.py new file mode 100644 index 0000000..8a788f9 --- /dev/null +++ b/zkml-research/KYA_face/helper.py @@ -0,0 +1,63 @@ +from sklearn import tree +from sklearn.utils.multiclass import unique_labels + +def plot_mlp_architecture(clf, ax=None): + """ + Plot the architecture of a Multi-layer Perceptron (MLP) classifier or regressor. + """ + import matplotlib.patches as mpatches + import matplotlib.pyplot as plt + n_layers = clf.n_layers_ + layer_sizes = [clf.coefs_[0].shape[0]] + [w.shape[1] for w in clf.coefs_] + if ax is None: + fig, ax = plt.subplots(figsize=(8, 4)) + # Draw nodes + y_offset = 0 + for i, n in enumerate(layer_sizes): + x = i * 2 + for j in range(n): + ax.add_patch(mpatches.Circle((x, j - n/2), 0.2, color='skyblue', ec='k')) + if i == 0: + ax.text(x, n/2 + 0.5, "Input\n({})".format(n), ha='center') + elif i == len(layer_sizes) - 1: + ax.text(x, n/2 + 0.5, "Output\n({})".format(n), ha='center') + else: + ax.text(x, n/2 + 0.5, "Hidden\n({})".format(n), ha='center') + # Draw connections + for i in range(len(layer_sizes) - 1): + x0, x1 = i * 2, (i + 1) * 2 + for j in range(layer_sizes[i]): + for k in range(layer_sizes[i+1]): + ax.plot([x0, x1], [j - layer_sizes[i]/2, k - layer_sizes[i+1]/2], color='gray', lw=0.5, alpha=0.5) + ax.axis('off') + ax.set_title("MLP Architecture") + plt.show() + +def summarize_mlp(clf): + """ + Print the architecture (layer sizes) and total number of parameters + for an sklearn MLPClassifier or MLPRegressor. + + Parameters + ---------- + clf : object + A fitted sklearn.neural_network.MLPClassifier or MLPRegressor. + """ + # Reconstruct full layer sizes (input, hidden…, output) + layer_sizes = ( + [clf.coefs_[0].shape[0]] + + list(clf.hidden_layer_sizes) + + [clf.coefs_[-1].shape[1]] + ) + + # Print architecture + print("Layer sizes (including input and output):", layer_sizes) + print(f" ➔ Total layers (including input layer): {len(layer_sizes)}") + print(f" ➔ Hidden layers: {len(clf.hidden_layer_sizes)}") + print(" ➔ Output layer: 1") + + # Compute total parameters (weights + biases) + total_params = sum( + w.size + b.size for w, b in zip(clf.coefs_, clf.intercepts_) + ) + print(f"Total parameters: {total_params:,}") \ No newline at end of file diff --git a/zkml-research/KYA_face/requirements.txt b/zkml-research/KYA_face/requirements.txt new file mode 100644 index 0000000..c2300e1 --- /dev/null +++ b/zkml-research/KYA_face/requirements.txt @@ -0,0 +1,13 @@ +# ------------------------------------------------- +# KYA Face ID demo – pinned, known-good versions +# ------------------------------------------------- +numpy>=1.26,<2.0 +pandas>=2.2,<3.0 +matplotlib>=3.9,<4.0 +scikit-learn>=1.5,<1.6 +Pillow>=10.3,<11.0 +face_recognition==1.3.0 # needs CMake & compiler +face_recognition_models>=0.3.0 +zkml>=0.0.2b1 +jupyterlab>=4.2,<5.0 +ipykernel>=6.29,<7.0 \ No newline at end of file From 7a7949b460837831cd45d15c9f1f3e63f686723b Mon Sep 17 00:00:00 2001 From: Konstantin Pandl <16907747+kpandl@users.noreply.github.com> Date: Wed, 30 Jul 2025 19:07:41 +0200 Subject: [PATCH 06/31] complete README --- zkml-research/KYA_face/README.md | 48 ++++++++++++++++++++++++++------ 1 file changed, 40 insertions(+), 8 deletions(-) diff --git a/zkml-research/KYA_face/README.md b/zkml-research/KYA_face/README.md index 14a3b3c..402ea56 100644 --- a/zkml-research/KYA_face/README.md +++ b/zkml-research/KYA_face/README.md @@ -1,8 +1,40 @@ -# KYA workshop - -Steps to setup: -* Create & activate a virtual env called .venv `python -m venv .venv` -* Activate it - * Windows: `.venv\Scripts\activate` - * macOS/Linux: `source .venv/bin/activate` -* install the required Python libraries: `pip install -r requirements.txt` \ No newline at end of file +# KYA Workshop — Local Setup Guide + +## 1 Prerequisites (install once) + +| Tool | Windows | macOS | Linux (Ubuntu) | +|------|---------|-------|----------------| +| **Python 3.10 or 3.11 × 64-bit** | [python.org](https://www.python.org) installer | `brew install python@3.11` | `sudo apt install python3.11 python3.11-venv` | +| **C++ build tools**
(needed for `dlib` → `face_recognition`) | *Visual Studio Build Tools 2022* → “Desktop C++” workload | `xcode-select --install` | `sudo apt install build-essential` | +| **CMake ≥ 3.22** | [cmake.org](https://cmake.org) installer | `brew install cmake` | `sudo apt install cmake` | +| **Leo CLI** | Follow steps at | Follow steps at | Follow steps at | + +--- + +## 2 Clone the repo & create a virtual env + +```bash +git clone https://github.com//kya-workshop.git +cd kya-workshop + +# Create & activate a venv named .venv +python -m venv .venv +# Windows +.venv\Scripts\activate +# macOS/Linux +source .venv/bin/activate +``` + +--- + +## 3 Install Python dependencies + +```bash +pip install -r requirements.txt +``` + +--- + +## 4 Open the Jupyter notebook + +E.g., through VS code, or through running `jupyter lab` \ No newline at end of file From f2371fd85f0eb0ed8da79a2d8c4a7fbda8b4a0b2 Mon Sep 17 00:00:00 2001 From: Konstantin Pandl <16907747+kpandl@users.noreply.github.com> Date: Wed, 30 Jul 2025 19:54:35 +0200 Subject: [PATCH 07/31] update zkml req --- zkml-research/KYA_face/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/zkml-research/KYA_face/requirements.txt b/zkml-research/KYA_face/requirements.txt index c2300e1..6ad49bc 100644 --- a/zkml-research/KYA_face/requirements.txt +++ b/zkml-research/KYA_face/requirements.txt @@ -8,6 +8,6 @@ scikit-learn>=1.5,<1.6 Pillow>=10.3,<11.0 face_recognition==1.3.0 # needs CMake & compiler face_recognition_models>=0.3.0 -zkml>=0.0.2b1 +zkml>=0.0.2b2 jupyterlab>=4.2,<5.0 ipykernel>=6.29,<7.0 \ No newline at end of file From 0a967810ab55820147c0a84ff122629f404d9b1c Mon Sep 17 00:00:00 2001 From: Konstantin Pandl <16907747+kpandl@users.noreply.github.com> Date: Wed, 30 Jul 2025 19:59:37 +0200 Subject: [PATCH 08/31] add face images --- .../negative/12_0_0_20170110215739155.jpg | Bin 0 -> 214273 bytes .../negative/14_0_1_20170117141604244.jpg | Bin 0 -> 66544 bytes 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